{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import numpy as np\n",
    "\n",
    "tasks = [\"mmlu\", \"tydiqa\", \"bbh\"]\n",
    "ckpts = [105, 211, 317, 420]\n",
    "model_name=\"llama-2-7b-hf\"\n",
    "results_dir = f'/scratch/gpfs/mengzhou/space10/final/loss/{model_name}'\n",
    "pretrain_loss_file = os.path.join(results_dir, \"{}\", model_name)\n",
    "random_selection_loss_file = os.path.join(results_dir, \"{}\", \"p0.05_seed{}_lora_ckpt{}_random\")\n",
    "less_selection_loss_file = os.path.join(results_dir, \"{}\", \"p0.05_seed{}_lora_ckpt{}_less\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_loss(loss_file):\n",
    "    if not loss_file.endswith(\"txt\"):\n",
    "        loss_file = os.path.join(loss_file, \"loss.txt\")\n",
    "    with open(loss_file, 'r') as f:\n",
    "        return json.load(f)[\"loss\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "pretrain_losses = {}\n",
    "for task in tasks:\n",
    "    pretrain_loss = get_loss(pretrain_loss_file.format(task))\n",
    "    pretrain_losses[task] = pretrain_loss\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_selection_losses = {}\n",
    "for task in tasks:\n",
    "    random_selection_losses[task] = {}\n",
    "    for ckpt in ckpts:\n",
    "        random_selection_losses[task][ckpt] = {}\n",
    "        losses = []\n",
    "        for seed in [3, 6, 9]: \n",
    "            loss = get_loss(random_selection_loss_file.format(task, seed, ckpt))\n",
    "            losses.append(loss)\n",
    "        random_selection_losses[task][ckpt][\"loss\"] = np.mean(losses)\n",
    "        random_selection_losses[task][ckpt][\"std\"] = np.std(losses)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "less_selection_losses = {}\n",
    "for task in tasks:\n",
    "    less_selection_losses[task] = {}\n",
    "    for ckpt in ckpts:\n",
    "        less_selection_losses[task][ckpt] = {}\n",
    "        losses = []\n",
    "        for seed in [3, 6, 9]:\n",
    "            loss = get_loss(less_selection_loss_file.format(task, seed, ckpt))\n",
    "            losses.append(loss)\n",
    "        less_selection_losses[task][ckpt][\"loss\"] = np.mean(losses)\n",
    "        less_selection_losses[task][ckpt][\"std\"] = np.std(losses)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "for task in random_selection_losses:\n",
    "    random_selection_losses[task][0] = {\"loss\": pretrain_losses[task], \"std\": 0}\n",
    "    less_selection_losses[task][0] = {\"loss\": pretrain_losses[task], \"std\": 0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(f\"/scratch/gpfs/mengzhou/space10/final/loss/{model_name}/loss_acc.csv\")\n",
    "dfs = [df[df[\"method\"] == \"Random\"], df[df[\"method\"] == \"Less\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x250 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Create a plot for each task\n",
    "fig, axes = plt.subplots(1, len(tasks), figsize=(10, 2.5))\n",
    "labels = ['Random', 'Less']\n",
    "\n",
    "\n",
    "for i, data in enumerate([random_selection_losses, less_selection_losses]):\n",
    "    df = dfs[i]\n",
    "    # Loop through each task and plot\n",
    "    for j, (ax, task) in enumerate(zip(axes, data.keys())):\n",
    "        task_data = df[df['task'] == task]\n",
    "        sns.lineplot(x='epoch', y='loss', data=task_data, ax=ax, marker='o', label=labels[i])\n",
    "        lower_bound = task_data[\"loss\"] - task_data[\"std\"] \n",
    "        upper_bound = task_data[\"loss\"] + task_data[\"std\"]\n",
    "        x_axis = task_data[\"epoch\"]\n",
    "        ax.fill_between(x_axis.values, lower_bound.values, upper_bound.values, alpha=.3)\n",
    "\n",
    "        # Set title and labels\n",
    "        ax.set_title(task_names[j])\n",
    "        ax.set_xlabel('Epoch')\n",
    "        ax.set_ylabel('Loss')\n",
    "\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "# plt.show()\n",
    "plt.savefig(f\"{model_name}_loss.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_accuracy_data = [\n",
    "    [55.8, 57.3, 55.0, 54.7, 54.5],\n",
    "    [53.1, 25.8, 26.9, 27.1, 36.0],\n",
    "    [22.2, 37.9, 39.5, 42.0, 40.3]\n",
    "]\n",
    "\n",
    "random_accuracy_std = [\n",
    "    [0.0, 1.3, 0.7, 0.9, 0.7],\n",
    "    [0.0, 8.4, 3.5, 3.4, 3.0],\n",
    "    [0.0, 4.0, 5.4, 5.2, 2.6]\n",
    "]\n",
    "    \n",
    "less_accuracy_data = [\n",
    "    [55.8, 55.8, 54.9, 54.6, 55.2],\n",
    "    [53.1, 31.8, 33.6, 32.1, 31.5],\n",
    "    [22.2, 45.1, 44.4, 41.6, 44.4],\n",
    "]\n",
    "\n",
    "less_accuracy_std = [\n",
    "    [0.0, 1.1, 0.7, 0.9, 2.9],\n",
    "    [0.0, 7.3, 4.8, 5.4, 5.3],\n",
    "    [0.0, 4.4, 1.2, 0.7, 1.2]\n",
    "]\n",
    "\n",
    "\n",
    "# Creating the structured data dictionary\n",
    "rand_acc = {}\n",
    "for i, task in enumerate(tasks):\n",
    "    rand_acc[task] = {}\n",
    "    for j, checkpoint in enumerate([0] + ckpts):\n",
    "        rand_acc[task][checkpoint] = {\"accuracy\": random_accuracy_data[i][j],\n",
    "                                      \"std\": random_accuracy_std[i][j]}\n",
    "\n",
    "less_acc = {}\n",
    "for i, task in enumerate(tasks):\n",
    "    less_acc[task] = {}\n",
    "    for j, checkpoint in enumerate([0] + ckpts):\n",
    "        less_acc[task][checkpoint] = {\"accuracy\": less_accuracy_data[i][j],\n",
    "                                      \"std\": less_accuracy_std[i][j]}\n",
    "\n",
    "methods = [\"Random\", \"Less\"]\n",
    "dfs = []\n",
    "for i, data in enumerate([random_selection_losses, less_selection_losses]):\n",
    "    # Convert to DataFrame\n",
    "    df_list = []\n",
    "    for task, checkpoints in data.items():\n",
    "        for epoch, (checkpoint, metrics) in enumerate(sorted(checkpoints.items(), key=lambda x: x[0])):\n",
    "            df_list.append({'task': task, 'checkpoint': checkpoint, 'loss': metrics['loss'], 'std': metrics['std'], 'epoch': epoch, \"accuracy\": rand_acc[task][checkpoint][\"accuracy\"] if i == 0 else less_acc[task][checkpoint][\"accuracy\"],\n",
    "                            \"accuracy_std\": rand_acc[task][checkpoint][\"std\"] if i == 0 else less_acc[task][checkpoint][\"std\"], \"method\": labels[i]})\n",
    "    df = pd.DataFrame(df_list)\n",
    "    dfs.append(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x250 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Create a plot for each task\n",
    "fig, axes = plt.subplots(1, len(tasks), figsize=(10, 2.5))\n",
    "labels = ['Random', 'Less']\n",
    "for i, data in enumerate([random_selection_losses, less_selection_losses]):\n",
    "    df = dfs[i] \n",
    "\n",
    "    # Loop through each task and plot\n",
    "    for ax, task in zip(axes, data.keys()):\n",
    "        task_data = df[df['task'] == task]\n",
    "        sns.lineplot(x='epoch', y='loss', data=task_data, ax=ax, marker='o', label=labels[i])\n",
    "        lower_bound = task_data[\"loss\"] - task_data[\"std\"] \n",
    "        upper_bound = task_data[\"loss\"] + task_data[\"std\"]\n",
    "        x_axis = task_data[\"epoch\"]\n",
    "        ax.fill_between(x_axis.values, lower_bound.values, upper_bound.values, alpha=.3)\n",
    "\n",
    "        # Set title and labels\n",
    "        ax.set_title(task)\n",
    "        ax.set_xlabel('Epoch Number')\n",
    "        ax.set_ylabel('Loss')\n",
    "\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"{model_name}_loss.pdf\", bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x250 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Create a plot for each task\n",
    "fig, axes = plt.subplots(1, len(tasks), figsize=(10, 2.5))\n",
    "labels = ['Random', 'Less']\n",
    "\n",
    "for i, data in enumerate([random_selection_losses, less_selection_losses]):\n",
    "    # Convert to DataFrame\n",
    "    df = dfs[i] \n",
    "\n",
    "    # Loop through each task and plot\n",
    "    for ax, task in zip(axes, data.keys()):\n",
    "        task_data = df[df['task'] == task]\n",
    "        sns.lineplot(x='epoch', y='accuracy', data=task_data, ax=ax, marker='o', label=labels[i])\n",
    "        lower_bound = task_data[\"accuracy\"] - task_data[\"accuracy_std\"]\n",
    "        upper_bound = task_data[\"accuracy\"] + task_data[\"accuracy_std\"]\n",
    "        x_axis = task_data[\"epoch\"]\n",
    "        ax.fill_between(x_axis.values, lower_bound.values, upper_bound.values, alpha=.3)\n",
    "        # Set title and labels\n",
    "        ax.set_title(task)\n",
    "        ax.set_xlabel('Epoch Number')\n",
    "        ax.set_ylabel('Accuracy')\n",
    "\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>task</th>\n",
       "      <th>checkpoint</th>\n",
       "      <th>loss</th>\n",
       "      <th>std</th>\n",
       "      <th>epoch</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>accuracy_std</th>\n",
       "      <th>method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>mmlu</td>\n",
       "      <td>0</td>\n",
       "      <td>1.522453</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>61.100000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>mmlu</td>\n",
       "      <td>105</td>\n",
       "      <td>0.940245</td>\n",
       "      <td>0.005950</td>\n",
       "      <td>1</td>\n",
       "      <td>60.116959</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>mmlu</td>\n",
       "      <td>211</td>\n",
       "      <td>1.018106</td>\n",
       "      <td>0.043578</td>\n",
       "      <td>2</td>\n",
       "      <td>62.690058</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>mmlu</td>\n",
       "      <td>317</td>\n",
       "      <td>1.225953</td>\n",
       "      <td>0.018643</td>\n",
       "      <td>3</td>\n",
       "      <td>62.105263</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>mmlu</td>\n",
       "      <td>420</td>\n",
       "      <td>1.479622</td>\n",
       "      <td>0.094117</td>\n",
       "      <td>4</td>\n",
       "      <td>62.690058</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>tydiqa</td>\n",
       "      <td>0</td>\n",
       "      <td>0.411331</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>24.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>tydiqa</td>\n",
       "      <td>105</td>\n",
       "      <td>0.308413</td>\n",
       "      <td>0.013250</td>\n",
       "      <td>1</td>\n",
       "      <td>40.035273</td>\n",
       "      <td>5.1</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>tydiqa</td>\n",
       "      <td>211</td>\n",
       "      <td>0.304884</td>\n",
       "      <td>0.032496</td>\n",
       "      <td>2</td>\n",
       "      <td>37.399442</td>\n",
       "      <td>5.5</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>tydiqa</td>\n",
       "      <td>317</td>\n",
       "      <td>0.309170</td>\n",
       "      <td>0.018366</td>\n",
       "      <td>3</td>\n",
       "      <td>47.160494</td>\n",
       "      <td>8.4</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>tydiqa</td>\n",
       "      <td>420</td>\n",
       "      <td>0.352304</td>\n",
       "      <td>0.045243</td>\n",
       "      <td>4</td>\n",
       "      <td>51.060276</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>bbh</td>\n",
       "      <td>0</td>\n",
       "      <td>0.232855</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>bbh</td>\n",
       "      <td>105</td>\n",
       "      <td>0.238434</td>\n",
       "      <td>0.008617</td>\n",
       "      <td>1</td>\n",
       "      <td>35.390946</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>bbh</td>\n",
       "      <td>211</td>\n",
       "      <td>0.249598</td>\n",
       "      <td>0.015099</td>\n",
       "      <td>2</td>\n",
       "      <td>39.917695</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>bbh</td>\n",
       "      <td>317</td>\n",
       "      <td>0.263970</td>\n",
       "      <td>0.023206</td>\n",
       "      <td>3</td>\n",
       "      <td>42.386831</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>bbh</td>\n",
       "      <td>420</td>\n",
       "      <td>0.257859</td>\n",
       "      <td>0.003376</td>\n",
       "      <td>4</td>\n",
       "      <td>37.448560</td>\n",
       "      <td>8.2</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0    task  checkpoint      loss       std  epoch   accuracy   \n",
       "15          15    mmlu           0  1.522453  0.000000      0  61.100000  \\\n",
       "16          16    mmlu         105  0.940245  0.005950      1  60.116959   \n",
       "17          17    mmlu         211  1.018106  0.043578      2  62.690058   \n",
       "18          18    mmlu         317  1.225953  0.018643      3  62.105263   \n",
       "19          19    mmlu         420  1.479622  0.094117      4  62.690058   \n",
       "20          20  tydiqa           0  0.411331  0.000000      0  24.200000   \n",
       "21          21  tydiqa         105  0.308413  0.013250      1  40.035273   \n",
       "22          22  tydiqa         211  0.304884  0.032496      2  37.399442   \n",
       "23          23  tydiqa         317  0.309170  0.018366      3  47.160494   \n",
       "24          24  tydiqa         420  0.352304  0.045243      4  51.060276   \n",
       "25          25     bbh           0  0.232855  0.000000      0   7.400000   \n",
       "26          26     bbh         105  0.238434  0.008617      1  35.390946   \n",
       "27          27     bbh         211  0.249598  0.015099      2  39.917695   \n",
       "28          28     bbh         317  0.263970  0.023206      3  42.386831   \n",
       "29          29     bbh         420  0.257859  0.003376      4  37.448560   \n",
       "\n",
       "    accuracy_std method  \n",
       "15           0.0   Less  \n",
       "16           1.8   Less  \n",
       "17           2.0   Less  \n",
       "18           1.4   Less  \n",
       "19           0.2   Less  \n",
       "20           0.0   Less  \n",
       "21           5.1   Less  \n",
       "22           5.5   Less  \n",
       "23           8.4   Less  \n",
       "24           0.6   Less  \n",
       "25           0.0   Less  \n",
       "26           3.1   Less  \n",
       "27           2.9   Less  \n",
       "28           5.0   Less  \n",
       "29           8.2   Less  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAADwCAYAAAAdFAfzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACTGUlEQVR4nOzdeXyU5bXA8d/smSSTfSdA2DREICAIImpRUVTEDatVK1ZtrRVaFdt7pd6KWhW1Vq3WSosKLiCWilRtRRFFREERCFvYt5B9ncy+z/1jyEBIAknIZGaS8/185iPzzjvvPBPhyXue5RyF3+/3I4QQQgghhBBCiC6nDHcDhBBCCCGEEEKInkqCbiGEEEIIIYQQIkQk6BZCCCGEEEIIIUJEgm4hhBBCCCGEECJEJOgWQgghhBBCCCFCRIJuIYQQQgghhBAiRCToFkIIIYQQQgghQkSCbiGEEEIIIYQQIkQk6BZCCCGEEEIIIUJEgm4hhBCiB1AoFDz66KPhboYQQgghTiBBt+i0hQsXolAoUCgUrF27tsXrfr+fvn37olAouOqqq4LHm97z85//vNXrPvzww8Fzamtrg8d/9rOfER8ff8r2/PDDD62+ftVVV5GXl9fObyeEEB3T1G+d6rF69epTXisvLy94vlKpJCkpieHDh3P33Xfz3XffdahdO3bs4Kc//Sl9+vRBp9ORk5PDT3/6U4qLi0/6vr/97W8oFArGjRvXoc8TQoiudvw9Z9MjIyODiy66iE8++aTZuSeeFxcXR0FBAU888QQ2m63Zuae6t1QoFMycOTMk30n0LupwN0BEv5iYGBYvXsz555/f7PhXX31FaWkpOp2u1fe8//77/O1vf0Or1TZ77d133yUmJgaHwxHSdgshRFd6++23mz1/6623WLlyZYvjQ4cObdf1Ro4cyYMPPgiA2Wxm586dLF26lPnz5/PAAw/w/PPPNzvfbrejVjf/tb5s2TJuvvlmUlJSuOuuuxgwYACHDh3i9ddf51//+hfvvfce11xzTaufv2jRIvLy8vj+++/Zt28fgwcPble7hRAiVB5//HEGDBiA3++nqqqKhQsXcuWVV/LRRx81m+C59NJLmT59OgAWi4Wvv/6aP/zhD2zZsoWlS5eGq/miF5OgW5y2K6+8kqVLl/LSSy81u+FbvHgxo0ePbjZb3eTyyy/nww8/5JNPPml2w/ftt99y8OBBpk2bxvvvv98t7RdCiK7w05/+tNnz9evXs3LlyhbH26tPnz4t3vvMM89wyy238MILLzBkyBB+9atfBV+LiYlpdu7+/fu57bbbGDhwIGvWrCE9PT342n333ccFF1zAT3/6U7Zu3cqAAQOavffgwYN8++23LFu2jF/+8pcsWrSIOXPmdOp7CCFEV7niiisYM2ZM8Pldd91FZmYm7777brOg+4wzzmjWf95zzz24XC6WLVuGw+Fo0V8KEWqyvFyctptvvpm6ujpWrlwZPOZyufjXv/7FLbfc0up7+vTpw4UXXsjixYubHV+0aBHDhw9n2LBhIW2zEEJ0t9tvv520tDTcbneL1y677DLOPPPMU15Dr9fz9ttvk5KSwpNPPonf7w++duKe7j/96U/YbDb+8Y9/NAu4AdLS0vj73/+OxWLhT3/6U4vPWbRoEcnJyUyZMoUbbriBRYsWdeCbCiFE90hKSkKv17dY5dOarKwsFApFu84VoqtJ0C1OW15eHuPHj+fdd98NHvvkk09obGzkJz/5SZvvu+WWW/joo4+wWCwAeDweli5d2magLoQQ0ey2226jrq6OTz/9tNnxyspKvvjii3bPiMfHx3PddddRVlZ20n3ZH330EXl5eVxwwQWtvn7hhReSl5fHRx991OK1RYsWcf3116PVarn55pvZu3cvGzZsaFf7hBAiVBobG6mtraWmpoYdO3bwq1/9CovF0qL/dDgc1NbWUltby+HDh1m8eDFvvvkmt9xyS6tBd9O5Jz6E6CoSdIsuccstt7B8+XLsdjsQuGH70Y9+RE5OTpvvueGGG/B6vSxfvhyAzz77jNraWm6++ebuaLIQQnSriy++mNzcXN55551mx9999118Pl+HlqE3rQbav39/q683NjZSXl5OYWHhSa8zYsQISktLMZvNwWMbN25k165dwUHT888/n9zcXJntFkKE3aRJk0hPTycjI4Nhw4axcOFC3njjDS699NJm573++uukp6eTnp5OXl4et956K5dccgnz589vcU2r1Ro898SHEF1Fgm7RJW688Ubsdjsff/wxZrOZjz/++JQz1snJyVx++eXBGfLFixdz3nnn0b9//+5oshBCdCulUsmtt97Khx9+2CzIXbRoEeedd16LfdUn05Rt9/jrHK/puMFgOOl1ml4/sT2ZmZlcdNFFQGDZ+k033cSSJUvwer3tbqMQQnS1V155hZUrV7Jy5UreeecdLrroIn7+85+zbNmyZuddc801wfP+/e9/M3v2bFasWMEtt9zSbFsOBPJhNJ174kOIriKbGkSXSE9PZ9KkSSxevBibzYbX6+WGG2445ftuueUWbrvtNkpKSli+fDnPPvtsSNupUChCen0hhDiZ6dOn88wzz/DBBx8wffp0du/ezcaNG5k3b16HrtO0LaetoLq1YLo1ZrMZhUJBWloaAF6vlyVLlnDRRRdx8ODB4Hnjxo3jz3/+M6tWreKyyy7rUFuFEKKrjB07tlkitZtvvplRo0Yxc+ZMrrrqqmBFnNzcXCZNmhQ87+qrryY1NZXf/va3fPzxx0ydOjX4mkqlanauEKEgM92iy9xyyy188sknzJs3jyuuuIKkpKRTvufqq69Gp9Nx++2343Q6ufHGGzv9+U2ZKJuWuJ/IZrNJtkohRFgVFBQwevTo4BLzd955B61W2+G+b/v27QBtlvFKTEwkJyeHrVu3nvQ6W7duJTc3N3ij+sUXX1BRUcGSJUsYMmRI8NHUPlliLoSIJEqlkosuuoiKigr27t170nMvueQSANasWdMdTROiGQm6RZe57rrrUCqVrF+/vt3J0PR6Pddeey2rV6/m0ksvDc62dEbTsvTdu3e3+vqePXtk6boQIuymT58eDG4XL17MlClTSE5Obvf7LRYLH3zwAX379j1pze+pU6dy8OBB1q5d2+rrX3/9NYcOHeLHP/5x8NiiRYvIyMhg6dKlLR4333wzH3zwQZsDm0IIEQ4ejwc4tgLodM8TIhRkebnoMvHx8bz66qscOnSo2bKdU/ntb3/LoEGDmDx58ml9/ujRo8nIyOC1117jtttuQ6fTBV9bvnw5ZWVl/O53vzutzxBCiNN188038+CDD3Lfffdx4MCBVkt2tcVut3PbbbdRX1/PU089ddItM7/97W95++23+eUvf8maNWtITU0NvlZfX88999xDQkICM2fODF572bJl/PjHP251e1BOTg7vvvsuH374ITfddFMHvrEQQoSG2+3ms88+Q6vVnnQQEghWajhVgkkhQkGCbtGlbr/99g6/p7CwsN0doNvt5oknnmhxPCUlhXvvvZfnnnuO22+/nXPOOYebbrqJ1NRUNm/ezBtvvMGIESO4++67O9w+IYToSunp6Vx++eUsXbqUpKQkpkyZ0up5ZWVlwWXoFouF4uJili5dSmVlJQ8++CC//OUvT/o5gwcP5q233uLmm29m+PDh3HXXXQwYMIBDhw7x+uuv09DQwJIlS4IJ3JoSvF199dWtXu/cc88lPT2dRYsWSdAthAiLTz75hF27dgFQXV3N4sWL2bt3Lw899BAJCQnB8/bs2RPsP202G+vXr+fNN99k8ODB3HbbbWFpu+jdJOgWUcXlcvGHP/yhxfFBgwZx7733ctttt5Gens6zzz7Ls88+i91uJzc3l9/85jf84Q9/QK/Xh6HVQgjR3PTp0/n444+58cYbm63KOV5RURG33XYbCoUCg8FA3759mTp1Kj//+c8ZO3Zsuz5n2rRpbNq0iblz5/Laa69RXV2Nz+cjJiaGjRs3UlBQEDx30aJFxMTEtCi900SpVDJlyhQWLVpEXV1ds5lzIYToDo888kjwzzExMeTn5/Pqq6+2GIQ8Pvu4SqUiOzubn//85/zxj38kLi6uW9ssBIDCf2LefCGEEEKE1L///W+uvfZa1qxZwwUXXNCtn/3WW2/xs5/9jJ/+9Ke89dZb3frZQgghRG8kM91CCCFEN5s/fz4DBw7k/PPP7/bPnj59OhUVFTz00EPk5uby1FNPdXsbhBBCiN5EZrqFEEKIbrJkyRK2bt3K3Llz+ctf/sJvfvObcDdJCCGEECEmQbcQQgjRTRQKBfHx8dx0003MmzcPtVoWnAkhhBA9nfy2F0IIIbqJjHMLIYQQvY8ynB++Zs0apk6dSk5ODgqFguXLl5/yPU6nk4cffpj+/fuj0+nIy8vjjTfeCH1jhRBCCCGEEEKIDgrrTLfVaqWwsJA777yT66+/vl3vufHGG6mqquL1119n8ODBVFRU4PP5QtxSIYQQQgghhBCi48IadF9xxRVcccUV7T5/xYoVfPXVVxw4cICUlBQA8vLyOvSZHo+HzZs3k5mZiVIZ1ol+IaKWz+ejqqqKUaNGyZ7UHkz6SyFOj/SVvYP0lUKcnt7QV0bVt/rwww8ZM2YMzz77LG+//TZxcXFcffXV/PGPf0Sv17frGps3b2bs2LEhbqkQvcP333/POeecE+5miBCR/lKIriF9Zc8mfaUQXaMn95VRFXQfOHCAtWvXEhMTwwcffEBtbS333nsvdXV1LFiwoNX3OJ1OnE5n8HlsbCwQ+J+anZ3dLe0WoqepqKhg7NixZGZmhrspIoSa/v9KfylE50hf2TtIXynE6ekNfWVUBd0+nw+FQsGiRYtITEwE4Pnnn+eGG27gb3/7W6uz3XPnzuWxxx5rcTw7O5vc3NyQt1mInkyW0fVsTf9/pb8U4vRIX9mzSV8pRNfoyX1lVH2z7Oxs+vTpEwy4AYYOHYrf76e0tLTV98yePZvGxsbgo7i4uLuaK4QQ4iTsLg8uj486ixOXx4fN5Ql3k4QQQgghulxUzXRPmDCBpUuXYrFYiI+PB2DPnj0olco2RxZ1Oh06nS743GQydUtbhRBCtM3p9jLvqwMs+PYgJruHBL2aO84bwL0TB6HTqMLdPCGEEEKILhPWmW6LxUJRURFFRUUAHDx4kKKiIkpKSoDALPX06dOD599yyy2kpqZyxx13UFxczJo1a/jd737HnXfe2e5EakIIIcLL7vLwt9X7+cuqvZjsgdltk93DX1bt5W+r98uMtxBCCCF6lLAG3T/88AOjRo1i1KhRAMyaNYtRo0bxyCOPAIFN9U0BOEB8fDwrV67EaDQyZswYbr31VqZOncpLL70UlvYLIYToOJVSyYJvD7b62oJvD6LuwXu6hBBCCNH7hHV5+cSJE/H7/W2+vnDhwhbH8vPzWblyZQhbJUR0c7i9xMjyXBHBzA53cIb7RCa7B7PDTWq8rtXXhRBCiJ7E6fHi8vgwxGjC3RQRQlG1p1v0fC6Xi7lz5wKB7QVarTbMLYou1SYHKJCgW0Q0Q4yGBL261cA7Qa+WG48eQPpyIYQ4NZfLxdNH+8qHHnqoWR4q0bPIGj4hegi/38++aku4myHEKXl9Pu44b0Crr91x3gA8Pl83t0gIIYTofiX11uCfSxvsYWyJCDWZ6RaiAyJ59qa0wY7N5Q13M4Q4Jb1Wzb0TBwFI9nIhhBC9ksnh5mDtsaD7YJ2VPqkGWa3YQ0nQLUQP4PH6mnXcQkQ6nUbFL380kBkXDabe6iJBr6a43CQBtxBCiB7P6/OzvbSR4xd2eb1+dleaKeybFLZ2idCRoFuExkcfde593uNmav/7X1B18gZ86tTOvS8SdOJnV220E9to58u6Q3wDzC4sRNsbf3YiqsRqA7+C7G4PV770NSa7m+9+f4kkURNCCNGj7ao0tbo6scbspNrsIMMQE4ZWiVCSPd1CRDmXx0utxYlaoeSatIHcN3RY5wNuIcJgQFo8OUkxeHx+lheVh7s5QgghRMhUmRxUGB1tvr6n0oLHGxm5TVwuF4899hiPPfYYLpcr3M2JajLT3ctE8p7kbtWDZuIrG51Eet6psrIy/vd//5dPPvkEm83G4MGDWbBgAWPGjAECSeDmzJnD/PnzMRqNTJgwgVdffZUhQ4aEueXd55VXXuFPf/oTlZWVFBYW8vLLLzN27NhTvm/JkiXcfPPNXHPNNSxfvjx4PNp+pjeN6cv2sh28t6GEOyfkoVAowt0kIYQQoks53F52VphOec6BWitnZBq6qVWiO8hMdwjYXR5cHh91Ficujw+bq/V6tEKcLrvbS4MtskceGxoamDBhAhqNhk8++YTi4mL+/Oc/k5ycHDzn2Wef5aWXXmLevHl89913xMXFMXnyZByOtkeCe5L33nuPWbNmMWfOHDZt2kRhYSGTJ0+murr6pO87dOgQv/3tb7ngggtavBZtP9OrC/ugVSnZU2Vhe9nJb0iEEEKIaOP3+9le1ojH6z/luUfqbZgc7m5oleguMtN9Mp2YDXV7vHy2rZLPd1Vhc3mJ1aqYlJ/JlcOz0Kg7OCsqe2vFKZQb7fhP3XeH1TPPPEPfvn1ZsGBB8NiAAcfKRfn9fl588UX+7//+j2uuuQaAt956i8zMTJYvX85PfvKTbm9zd3v++ef5xS9+wR133AHAvHnz+M9//sMbb7zBQw891Op7vF4vt956K4899hhff/01RqMx+Fo0/kwTYzVMPiuTj7ZW8N6GEobnDg93k4QQQoguc6DWitHWvkDa74ddFWbOyUuWlV89hMx0dyGn28t/t1Xy4dbyYHIEm8vLh1vL+e+2SpxuKed0KlqVijlnn82cs8+OyH3JkdQ+s8ON2R7eVRRmsxmTyRR8OJ3OFud8+OGHjBkzhh//+MdkZGQwatQo5s+fH3z94MGDVFZWMmnSpOCxxMRExo0bx7p167rle4STy+Vi48aNzb6/Uqlk0qRJJ/3+jz/+OBkZGdx1110tXovWn+mN5/QFYHlROQ7pL4UQQvQQRpuLQx2sMmOyu6V2dw8iM91dSKlU8PmuqlZf+3xXFVeOyO7aD+zMvuQI3JMsOqfcGP6OuKCgoNnzOXPm8OijjzY7duDAAV599VVmzZrF73//ezZs2MBvfvMbtFott99+O5WVlQBkZmY2e19mZmbwtZ6strYWr9fb6vfftWtXq+9Zu3Ytr7/+OkVFRa2+3tmfqdPpbDZwYjab2/MVusx5g9LISoih0uTgs+Iqri7M6dbPF72H5DcRQnQXt9fH9jJTp1Ym7quxkG7QSe3uHkBmuruQ3eVtNf0/BGa87W28JkRHNVhd2F3hz55WXFxMY2Nj8DF79uwW5/h8Ps4++2yeeuopRo0axd13380vfvEL5s2bF4YWRz+z2cxtt93G/PnzSUtL69Jrz507l8TExODjxEGVUFMpFfx4TC4Ai7873K2fLYQQQoTCrgpzp1dveb1+9lR17wC4CA2Z6e5Ceq2KWK2q1cA7VqtCr5VRKnH6fD4/FY2RkQzLYDCQkJBw0nOys7NbBG9Dhw7l/fffByArKwuAqqoqsrOPrQapqqpi5MiRXdvgCJSWloZKpaKqqvkqmaqqquDP5nj79+/n0KFDTD1utYnvaPp6tVrN7t27O/0znT17NrNmzQo+Lysr6/bA+8ej+/LyF/v47kA9ZUY7fZL03fr5IsqEsxKFrPgKi45Uepg/fz5vvfUW27dvB2D06NE89dRTzc63WCw89NBDLF++nLq6OgYMGMBvfvMb7rnnnm75PqJnKzPaqTKd3j1btclJjdlJukHXRa0S4SAz3V3I5/MzKT+z1dcm5Wfi80V4xisRFWqPZsWPFhMmTGD37t3Nju3Zs4f+/fsDgaRqWVlZrFq1Kvi6yWTiu+++Y/z48d3a1nDQarWMHj262ff3+XysWrWq1e+fn5/Ptm3bKCoqCj6uvvpqLrroIoqKiujbt2+nf6Y6nY6EhITgw2Do/nIl/VJjGdM/GT/w7ncl3f75onfQqlQ8NLwwIvJziPbraKWH1atXc/PNN/Pll1+ybt06+vbty2WXXUZZWVnwnFmzZrFixQreeecddu7cyf3338/MmTP58MMPu+triR7K5vKwp7JrZql3V5rxShwR1STo7kI6jYorh2dx9YgcYo/OasdqVVw9Iocrh2ehi4D9GJGUCEx0nMfrO+0R0+72wAMPsH79ep566in27dvH4sWL+cc//sGMGTMAUCgU3H///TzxxBN8+OGHbNu2jenTp5OTk8O1114b3sZ3k1mzZjF//nzefPNNdu7cya9+9SusVmswm/n06dODS/djYmIYNmxYs0dSUhIGg4Fhw4ah1Wqj/md689h+APxrY6kMVoqQsLk81Fkju9yiaOn4Sg8FBQXMmzeP2NhY3njjjVbPX7RoEffeey8jR44kPz+f1157LTio2eTbb7/l9ttvZ+LEieTl5XH33XdTWFjI999/311fS4SQy+Xiscce47HHHsPl6r5/8z6fn22ljV0WKDvcXvbXWLrkWiI8ZHl5F9OoVVw+LIsrR2RjsruJ16lpsLs6Xi5MiFZUm514TzLJrVaCMsJKS5xzzjl88MEHzJ49m8cff5wBAwbw4osvcuuttwbP+Z//+R+sVit33303RqOR888/nxUrVhATExPGlnefm266iZqaGh555BEqKysZOXIkK1asCCZCKykpQans2BhpNP9MrxyezR/+vZ1Kk4Nv9tVywRnp4W6S6GEqGh3oI2AgXLRfU6WH43OHtKfSw/FsNhtut5uUlJTgsfPOO48PP/yQO++8k5ycHFavXs2ePXt44YUXuvw7iB7shK0ulUY7OpOTUy0I9/iP3dSlrlmFWtH273q7AmyZ8cRqWwnfZLtLxJOgOwSaZrS3lhr5d1E5gzLi+fXFQ8LcKhHtnG4vNZaWJbkAdGol6Qk6EvVq7C4fbq8Pn88fEasrAK666iquuuqqNl9XKBQ8/vjjPP74493Yqsgyc+ZMZs6c2eprq1evPul7Fy5c2OJYNP9M9drA4OWyTWW8vf6wBN2iS5nsgXKLEnRHl85UejjR//7v/5KTk9OsnOLLL7/M3XffTW5uLmq1GqVSyfz587nwwgvbvE64Kz2IyGayu6k2tX6/djr8fihtsDMkI15qd0chCbpDaGB6PGanh+JyE063N2ICIBGdKk0O/K3McuvUSgakx7FiewWrdlVjc3mJ1aqYlJ/JlcOzZJWFiEq3juvPsk1lrN5dQ6PNRWKslHQSp8/v90dEuUXR/Z5++mmWLFnC6tWrm634efnll1m/fj0ffvgh/fv3Z82aNcyYMaNFcH68uXPn8thjj3VX00UUcXt9lNTbQnZ9m9NLrcUlSdWikOzpDqF+KbGkxmlxeX0UV5jC3RwRxWwuDw1Wd6uvpSfoWLG9go+2VgQz59tcXj7cWs5/t1Xi7GSZCiHC6ex+SfRN0ePy+nh3w5FwN0f0EHVWFw539CSiFMd0tNLD8Z577jmefvppPvvsM0aMGBE8brfb+f3vf8/zzz/P1KlTGTFiBDNnzuSmm27iueeea/N6s2fPblYus7i4+PS+nOgxjtTb8HhDm4ukotGOyyP3dsd7+umng/lsmjgcDmbMmEFqairx8fFMmzatRf/RnSToDiGFQsHIvskAbC4xhrcxIqqVG1tPnqZWQqJezapdrWdu/XxXFUqlLEES0UehUHDD2YGa3cs2lYa5NaIn8Hp9VEZIuUXRcR2t9NDk2Wef5Y9//CMrVqxgzJgxzV5zu9243e4WOTNUKlWwFGNrIqHSg4g8NWYnJrsn5J/j8wVKkYmADRs28Pe//73ZgBoEEvl+9NFHLF26lK+++ory8nKuv/76MLVSgu6QG9U3CQjs75ZU/6IzGu1uLI7WO3G1SonV6W21NjwEZrztbbwmRKS7eVw/lArYU2Vh0+H6cDdHRLkqszPkM1AitDpS6QHgmWee4Q9/+ANvvPEGeXl5VFZWUllZicUSyAKdkJDAj370I373u9+xevVqDh48yMKFC3nrrbe47rrrwvIdRXSyuTyUN3ZfINxo89Bob30FZG9isVi49dZbmT9/PsnJycHjjY2NvP766zz//PNcfPHFjB49mgULFvDtt9+yfv36sLRVgu4QG5IZT5xWjdnpkVT/osP8fj8VJxnN9Hh9xOlUwRJ1J4rVqtC38ZoQkS7DEMPYAYEsw+9IzW5xGlyethNRiujRtOz7kUceYeTIkRQVFbWo9FBRURE8/9VXX8XlcnHDDTeQnZ0dfBy/dHzJkiWcc8453HrrrRQUFPD000/z5JNPcs8993T79xPRyevzU1JnazXvTiiVNdh7/YTejBkzmDJlSov8Cxs3bsTtdjc7np+fT79+/dpd7aCrSSK1EFOrlIzITWTdgTo2lzRwRqYsQRLtV3+K/YceX2C08+L8DD7eWtHi9Un5mYE6xxJ3iyh145i+rD9Qz+fFVZgdbgwxmnA3qXc5oQxOu3mPW2Hz3/+CqpOdUBeVwalobD0RpYg+Han0cOjQoVNeLysriwULFnRBy0RvtafKjDMMuSJcHh9VJgc5Sfpu/+xQMZvNmEzH8mDpdDp0utaTxi1ZsoRNmzaxYcOGFq9VVlai1WpJSkpqdjwzM5PKysoubXN7SdDdDUb2TWLdgTqKjhi5cUxfSfMv2sXr81NpOvX+wzV7aphUEBjl/0Kyl4se5qoROTz64Q5MDg8fbCpj+nl54W6SiDJWZ9uJKIUQUaQzg4AhHgCsNjsoa7CT1rmrnrYas5OkWA2xYfr8rlZQUNDs+Zw5c3j00UdbnHfkyBHuu+8+Vq5c2awaQSSToLsbnNUnEbVSSbXZSXmjgz49aERKhE6N2YHbc/JlQzVmJ5/sqGRTiZGfX5DHVSOysbt86LUqfD6/BNwi6mnVSi47K5N/bSzj30Vl3Hpuf1SSHFB0gJQIE0KEgsPtZWdFeGu0B2t3+/09YlKvuLiYPn36BJ+3Ncu9ceNGqqurOfvss4PHvF4va9as4a9//SuffvopLpcLo9HYbLa7PdUOQkX2dHcDvUbF0OzAsvLNhxvC3BoRDdxeL9Xmk+8/dHu9fFpcic/vR69T0mBzs7vCDArQqJRSF170GDeP7QdA0ZFGdpQ1hrk1IpoYbS6sTkkmKYToWn6/nx3lJtye8O9bsTm9lDb0jMFFg8HQrDJAW0H3JZdcwrZt2ygqKgo+xowZw6233hr8s0ajaVbtYPfu3ZSUlJy02kEoyUx3NxnVL5ltZY0UHTFyVWFOuJsjIlyVyclJqpUAsHZfHQ02F3FaNZecmYkCBR4f+Py9O6mG6HlG909hYHocB2qs/POHI4w4WhVCiJPx+fxtllsUQojTcajORoPVFe5mBO2rsZBu0BHTSyZcDAYDw4YNa3YsLi6O1NTU4PG77rqLWbNmkZKSQkJCAr/+9a8ZP3485557bjiaLDPd3aWwbyIK4GCdNaL+kYrI43R7qbOc/O/IgVoL28sDM36XDs3sNZ2s6L2uGhEYrPxidzXV7ch10BVcLhePPfYYjz32GC6X9NvRptbixBUBs1BC9HS9ra9stLs5EGEVibxeP3uqwrvUPdK88MILXHXVVUybNo0LL7yQrKwsli1bFrb2SNDdTZL0WgakxwFQdMQY3saIiFZutHOyyWqr082qndVAoA5835Sekj5DiLbdNCYXjUpBudHBl7urw90cEeHc3kBWXyGE6Eoer48dZY0nvU8Ll2qTk5pTbE3syVavXs2LL74YfB4TE8Mrr7xCfX09VquVZcuWhW0/N0jQ3a1G9Q0Ubd8sQbdog9HmotHuafN1P34+31mNw+MlLU7L+IGp3dg6IcKnT3Is5+QFanZ/sq0Si7PtfydCVJmceGWSWwjRxXZVmrG5IjdPxO5Kc6+v3R2pJOjuRiOP7kPcXWnC5pIbRtHS3uqTL1faWmqkpMGGWqlg8llZqJTyT1j0HteOCmQ0/e5gPXtlGZ1og8Ptpc7Se2d7hBChUdnooLIxslfQONzeiFv6LgIkkVo3yknSk5UQQ6XJwfayRsYOkFlKcUy1yUGjzd1mrcdai5Nv9tUBcP7gNFLiWs/oKERPdflZWTz9yS7qrS4+2V7BWTmJaNUy8CSaqzjFFh0hhOgou8vLzkpTl19XrVByTdrALr1mSb2NrMQYDDGaYwc7U+McQl7nvDeRu5VuNrJfEgCbS4xhbYeILD6fn30nmeX2eH18WlyJ1+8nLzWOYX0Su7F1QkSGBL2GiWemA/D13lrKpP6yOIHZ4T7pFh0hRO+iVamYc/bZzDn7bLSdDBj9fj/byxvxeqNjNM/vDyyD98voY0QJa9C9Zs0apk6dSk5ODgqFguXLl7f7vd988w1qtZqRI0eGrH2hMOroEvNtpY24ZcOZOKrMaD/pHqFvDtRRb3URq1FxSX4GChTd2DohIse0swNLzHdVmCkqacDXS/eu9bZswe3h9/spl4EYIUQXq2gMrESMJo02d4+p3d1ThDXotlqtFBYW8sorr3TofUajkenTp3PJJZeEqGWhMyAtnoQYDXaPV1L7CyAwi32g1trm64fqLGwtNQIwaWgmsVrZFSJ6r5F9k8nPMuAHVu+uoboXZ2oVzdVbXdhdMpgthOg6Foc7ajOC76+x4HBHbtK33iasQfcVV1zBE088wXXXXdeh991zzz3ccsstjB8/PkQtCx2VUsHIvoGlwbLEXAAcqrPhbqOWrM3l4fOj5cFG5ibRPzWuO5smRMSJ06m5ZGgGAN/sr+VQXdsDVqL38Pr8VEqJMCFEF/J4fRyut0VtjgiP18/eKkmqFimibk/3ggULOHDgAHPmzGnX+U6nE5PJFHyYzeGfXR7ZL1A6rKjEKPstejmH28uRelurrzWVB7O7vaTGaTlvkCTeEwLgqhE56DUqai0ufjhUj9Emy6t7uxqzA7enZ/0+ffrpp1EoFNx///3BYw6HgxkzZpCamkp8fDzTpk2jqqoqfI0Uogc70mCL+n6lyuSgVqo5RISoWqe6d+9eHnroIb7++mvU6vY1fe7cuTz22GMhblnHDM0yoFMrabC7OFRnZUBafLibJMJkf42lzXqK20obOVxvRaVQcFmBlAcTokn/1FjOyUtmzd5a1u6r5cIz0kmK1Ya7WSJMXB5vj9tmsGHDBv7+978zYsSIZscfeOAB/vOf/7B06VISExOZOXMm119/Pd98802YWip6FMlwHVRrcdJo6xlJGXdXmkn2+VEpJR9QOEXNXbzX6+WWW27hscce44wzzmj3+2bPnk1jY2PwUVxcHMJWto9WreKsnMAS86IjxvA2RoSN2eFus95jndXJN/trAZgwOJW0eCkPJkSTWK2ay87KBGDj4QYO11ll31ovVtHowNeDtnJbLBZuvfVW5s+fT3JycvB4Y2Mjr7/+Os8//zwXX3wxo0ePZsGCBXz77besX78+jC0WomdxuL09Kimj3eWlSrbfhF3UBN1ms5kffviBmTNnolarUavVPP7442zZsgW1Ws0XX3zR6vt0Oh0JCQnBh8Fg6OaWt27U0dJhRbKvu9faV21pdZ+Q1+fjsx2VeHx++qfEMiI3qdvbJkSkmzAojZzEGNxeP98fbGhzm4bo2WwuD8Yoyyp8KjNmzGDKlClMmjSp2fGNGzfidrubHc/Pz6dfv36sW7euu5spRI/k8/k5VGftUQN5ADVmJzZXz5i5j1ZRE3QnJCSwbds2ioqKgo977rmHM888k6KiIsaNGxfuJnbIiNxElAoFpUY7NaaetSxOnFqdxUmdpfV9qN/ur6PW6kKvUTFpaGaHy4OpFL1v+VBeXh6PP/44JSUl4W6K6CZZiXomDE4D4Jt9gZrdbW3VED1XudERFUmOzGZzs/wyTmfrv/eXLFnCpk2bmDt3bovXKisr0Wq1JCUlNTuemZlJZWVlu9ohfaUQJ1dhcuDogVUQ/H6khFgHhKKvDOuebovFwr59+4LPDx48SFFRESkpKfTr14/Zs2dTVlbGW2+9hVKpZNiwYc3en5GRQUxMTIvj0SBep+GMjHh2VZnZfKSBy87KCneTRDfaV916Nskd5Y3B8mCXDM3ocHmw7KQYDDGa021e1Ln//vtZuHAhjz/+OBdddBF33XUX1113HTqdLMvvqfRaFZcWZPL+plIO1FopqbNRnm6nb0psy5PDuU8xgvYo9jQ1ZicWR3TM3BQUFDR7PmfOHB599NFmx44cOcJ9993HypUriYmJCUk7pK8Uom0mu7tHT4TZnF5qzE7SDfLv/VRC0VeGdab7hx9+YNSoUYwaNQqAWbNmMWrUKB555BEAKioqevRobFMW882yr7tXqbc6Mbdyo2h2uHnjm0MADO+TyIDUjiXYy0mKITMhNDdqke7++++nqKiI77//nqFDh/LrX/+a7OxsZs6cyaZNm8LdPBEiZ2YZGNEnCQjMdssS897D7/eztzr81Ujaq7i4uFl+mdmzZ7c4Z+PGjVRXV3P22WcHt9F99dVXvPTSS6jVajIzM3G5XBiNxmbvq6qqIiurfQP30lcK0Tq310tJL/gdUtlox+2VHCinEoq+MqxB98SJE/H7/S0eCxcuBGDhwoWsXr26zfc/+uijFBUVdUtbQ2Fk3yQA9laZMTt61p400Tqfz09lY8tRVL/fz5vrDtNod5MSq+X8QWkdum6fJD0ZvTTgPt7ZZ5/NSy+9RHl5OXPmzOG1117jnHPOYeTIkbzxxhtSoq+HyUyIYcLgQCm9bw/UYXK4qelhWaxF60ob7Nic0XPjaDAYmuWXaW225JJLLmmxjW7MmDHceuutwT9rNBpWrVoVfM/u3bspKSlh/PjxHWqP9JVCNFdSb8fj7fl/770+KGuQpGrt1ZV9ZVSVDOtp0g06+ibHcqTBxtbSxuD+RNFz1VicuDwt9wqt2VtL0REjaqWCy87KQq1q/3hYn2S9LBU6yu1288EHH7BgwQJWrlzJueeey1133UVpaSm///3v+fzzz1m8eHG4mym6SIxGxflD0nhr/WHMDg/by0xkJMTIv4cIoFWpmHP22SG5ttvr40CtNSTXDieDwdBiu1xcXBypqanB43fddRezZs0iJSWFhIQEfv3rXzN+/HjOPffcDn2W9JVCHFNjcmK2R8dWla5gtLkx2d0k6HvfdsSO6sq+UoLuMBvZN4kjDTY2lzRI0N3Debw+qlsp2VDRaOe9DUcAuP7sPqRX1bX7mhJwB2zatIkFCxbw7rvvolQqmT59Oi+88AL5+fnBc6677jrOOeecMLZShEKfpFjGD0zls+Iq1u6rZWTfJCxOD/E6+fXWUx2qteJuZfCyN3jhhRdQKpVMmzYNp9PJ5MmT+dvf/tbu90tfKURzNpeHclPvSzBW2mDnTJ1aane3IRR9pdyVhNmofkl8tLWcHeUmnG4vOk0nEvWIqFBlduI94T7R4/Ux/+uDuLw+hmYbmDQ0E6rad73cFL3U7z7qnHPO4dJLL+XVV1/l2muvRaNpOXo7YMAAfvKTn4ShdSKUMhJ0TBicxmfFVWwrbaTR7qakzkZBTkK4myZCwO7ycqSh5++7bHLiFruYmBheeeUVXnnllU5dT/pKIY7x+vwcrrPh74VjeC6PjyqTg5wkfbibEpFC0VdK0B1m/VJiSY3TUmd1UVxhDtbvFj2L0+2l1tJyr+nyonJK6m3E69TcNWEAynaW++qboidVAu6gAwcO0L9//5OeExcXx4IFC7qpRaK7xGhUnJWTwIC0OA7WWll/oI7kOA2DM+LRqqOmKqZop33Vlh5XP7c7SV8pQiGU20lCqazBjtPdezuUGrOTpFhNhyvl9Aah6CvlpxxmCoWCwtwkvthdTdGRBgm6e6iKRkeLkdSdFSY+3RGorXr7+P4kxWpPeR2FAnKTJeA+UXV1NZWVlYwbN67Z8e+++w6VSsWYMWPC1DLRHTITYjh/cBoHa618s6+WywoyKTPaGZAWF+6mnVo4y5lBVJU0a7S5qWpli45oP+krhQgw2lzUW13hbkZYNdXuPiPTEO6mRJxQ9JUyDRABRh0tHbbliBGvr+dnTuxtrE4PRlvz7PQWh4c3vjmIH7hwSFrw78DJKBQyw92WGTNmcOTIkRbHy8rKmDFjRhhaJLpTRoKOsQOS0agUlDc6OFhnpbTBhk/60x5lTxSVCItU0lcKAS6PV0pMHmVztr4SM9K9+uqrjBgxIlgRYvz48XzyySfB1x0OBzNmzCA1NZX4+HimTZtGVVU7928Smr5Sgu4IcEZmPLEaFWanh/01lnA3R3SxisbmCTr8+Hl7/WEabG4yE3TcNKbvKa+hUAS2IqTEScDdmuLiYs5uZWnbqFGjKC4uDkOLRHfSqVXkJOk5++jg1Tf76nC6fVRL+bAeo8rkoNEmpTVPl/SVorfz+/2U1Nta5NjpzSqM0Ve7Ozc3l6effpqNGzfyww8/cPHFF3PNNdewY8cOAB544AE++ugjli5dyldffUV5eTnXX399u68fir5Sgu4IoFYpGZGbCEDREWN4GyO6VKPdjcXRvCPbWW5mY0kDKqWCX1ww8JTJ85oC7uS4Uy8/jwZPP/00CoWC+++/P3jsdEckdTpdq+dXVFSgVssumt4gwxBYYg7w/cF6nB4vJTKT0SP4fH72VcuAdFeQvlL0dlVmZ4v7snBTKyFGoyRcaUiisXb31KlTufLKKxkyZAhnnHEGTz75JPHx8axfv57GxkZef/11nn/+eS6++GJGjx7NggUL+Pbbb1m/fn27rh+KvlKC7ggxsm9ghmZzSUOHCq2LyOX3+yk3Np/lNtpcrNlbA8C1I3PISz35nlOFAvql9pyAe8OGDfz9739nxIgRzY6f7ojkZZddxuzZs2lsbAweMxqN/P73v+fSSy/tsvaLyJWRoCM/20BavBa728vmEiMmuxujrXfv2esJSupt2F2RdZMcraSvFL2Z1emhqjFygkudWkluip4zsw1kJcZwZraB3GQ9ujBE3021u6OR1+tlyZIlWK1Wxo8fz8aNG3G73UyaNCl4Tn5+Pv369WPdunXtumYo+koZ1owQw3ITUSsVVJudVDRKCv+eoM7qapYV0+v38WlxJW6fj/wsA5PPyjrp+xVK6J8S264Ea9HAYrFw6623Mn/+fJ544ong8aYRycWLF3PxxRcDsGDBAoYOHcr69es599xzT3nt5557jgsvvJD+/fszatQoAIqKisjMzOTtt98OzRcSEUWnVpESp+O8QWl8uKWctftqOXdgKiX1NpLC3TjRaS6Pj0N11nA3o8eQvlL0Vl6vL1AeLELmtXRqJQPS41ixvYJVu6qxubzEalVMys9k8rAsDtZYcXq6dw18JNTuNpvNmEym4HOdTodO1/rWym3btjF+/HgcDgfx8fF88MEHFBQUUFRUhFarJSkpqdn5mZmZVFZWtqsdoegrZaY7Qug1KoZmB+rKbi5pCHNrxOny+vxUnjCa+v3BeqrNTnRqJXeeojyYQgn9U3tOwA2BpBRTpkxpNvIIdMmIZJ8+fdi6dSvPPvssBQUFjB49mr/85S9s27aNvn1PvWde9AyZCTomDEpFAeyqNFNrcVJjduLydG6WtKkMzpyzz0bb2ezg4rQcqLXg8YbmLjmM95VhI32l6I38fj8lDTZc3RzEnkx6go4V2yv4aGsFtqMreWwuLx9uLefT7ZWkG7o/h09T7e5wKigoIDExMfiYO3dum+eeeeaZFBUV8d133/GrX/2K22+/vctyU4Sir5SZ7ggysm8S28oa2XzEyJQROeFujjgNNWZHsxvFMqONjYcDgykX52eQdJLl4gol5KXGkajXhLydp6u9I5JLlixh06ZNbNiwocVrlZWVpz0iCYF6iXfffXf7Gy96nAxDDGkGHflZBnZWmvlmXy3XjOxDrcUlq4eikMPtpazBfuoTO0inVpKeoCNRr8Fkd6PXqvD5/KfMr9FTSF8pepvSBjuNNk+4mxGkVkKiXs2qXdWtvv75riquHJFFZSN09zhBjcVJcpwWfZj6w+LiYvr06RN83tYsN4BWq2Xw4MEAjB49mg0bNvCXv/yFm266CZfLhdFobHZvWVVVRVbWyVeZHq+r+0oJuiPIyL5JvL3+MAdrrTTYXJy6iJSIRG6vt1nWZKfHy8riKvxAQXYCg9MN1LbxXmUUBdwQGJE83pw5c3j00UebHTty5Aj33XcfK1euJCYmJqTtKS4upqSkBJer+T7eq6++OqSfKyKDVq0kOU7L+YPT2Flp5tv9dUwtzKHO4iTToEOlksVd0aTcaO/ypaAnW9J55fAsNOreEXhLXyl6i3KjnTpLZOX2UKuUWJ3e4Az3iWwuLxaH9+iKyO5dD+/3QWm9jSFHa3c3rfjqLgaDgYSEhE691+fz4XQ6GT16NBqNhlWrVjFt2jQAdu/eTUlJCePHj+/QNbuyr5SgO4IkxWoZmBbHgVorRSVGLgp3g0SnVDY68R0dmfTj58vd1ZidHhL1Gi4Yktbm+5RKGJGbROLe6Ai4oX0jkhs3bqS6urpZ6QWv18uaNWv461//yqeffnraI5IHDhzguuuuY9u2bSgUimAyQsXRJfzeKCiF8corr/CnP/2JyspKCgsLefnllxk7dmyr5y5btoynnnqKffv24Xa7GTJkCA8++CC33XZb8Jyf/exnvPnmm83eN3nyZFasWBHS7xFumQkxjOqXjF5TQp3Vxe5KMxk+qLe5w7JcT3SOye7GZO/6manjl3Q2aVrSCXD5sKwePePdE/pKIdqrxuyk2hR5pSM9Xh9xWhWxWlWrgXfs0dfeXHeI3ORY8rMTSI/vvt9f1qO1u9O68TM7avbs2VxxxRX069cPs9nM4sWLWb16NZ9++imJiYncddddzJo1i5SUFBISEvj1r3/N+PHj25UnCELTV3Zq2P/IkSOUlpYGn3///ffcf//9/OMf/+jM5cRxRvZNAqR0WLRyuL3UW4+Nhu2uNLO32oICuKwgs819oSqlgsLcpIju4FrTNCLZ9Ggt6L7kkkvYtm0bRUVFwceYMWO49dZbg39uGpFs0tERyfvuu48BAwZQXV1NbGwsO3bsYM2aNYwZM4bVq1d31ddtoav6wvfee49Zs2YxZ84cNm3aRGFhIZMnT6a6uvWlZykpKTz88MOsW7eOrVu3cscdd3DHHXfw6aefNjvv8ssvp6KiIvh49913O/4lo0yGQUeMVsnYASkArN0XWFdSKzW7o4bf76e8seuXlbdnSaeyh2/0jva+Uoj2arC6QrI9pSuUGu0UV5i5OD+j1dcvyc8I5CWxuigqNbJkQwnvfn+YLaUN2N3ds0w+0mt3V1dXM336dM4880wuueQSNmzYwKeffhrMLP7CCy9w1VVXMW3aNC688EKysrJYtmxZu68fir6yU0H3LbfcwpdffgkE9mNeeumlfP/99zz88MM8/vjjnWqICBjVL7CofFelCbMjOlP392YVxy2HbLS7WL07UB5s3IAUshJa31OqUioo7JtEapQF3O1lMBgYNmxYs0dcXBypqakMGzas2Yjkl19+ycaNG7njjjs6NCK5bt06Hn/8cdLS0lAqlSiVSs4//3zmzp3Lb37zm5B9t67qC59//nl+8YtfcMcdd1BQUMC8efOIjY3ljTfeaPX8iRMnct111zF06FAGDRrEfffdx4gRI1i7dm2z83Q6HVlZWcFHcnLP37SiUSlJjtUGa3ZvKmnA6fHi9PhojNJyKL1NvdWFw9X1Gxnbs6Szp5cmi/a+Uoj2MNndlDTYwt2MVh2osfDB5jKW/lDKpUMzmToim1htYEImVqvi6hE5XD4sG41KwdQR2QxOj0elUFBrdbFmby1vfHOI/24v50CtBa8/dBu+vT4oN0ZOebUTvf766xw6dAin00l1dTWff/55s1JeMTExvPLKK9TX12O1Wlm2bFmH9nOHoq/sVNC9ffv24LLHf/7znwwbNoxvv/2WRYsWsXDhwk41RARkJ8aQmRCDx+fnqz014W6O6ACL003j0eWQPr+fz4qrcPt85CTqGdM/pdX3NAXcKT2kDndnne6IpNfrxWAI7D9KS0ujvDywVLR///7s3r07JG2GrukLXS4XGzdubJa9XalUMmnSpHZlb/f7/axatYrdu3dz4YUXNntt9erVZGRkcOaZZ/KrX/2Kurq6k17L6XRiMpmCD7PZ3K7vEGkyE2LIS40lJykGt9fPnqrA96iR2e6I5/X5qQxR9lyP1xdcttmaWK0KfRuv9RTR3FcK0R4Wp5tDdVZCGI922rZSI//dXoHH50enVnKo1sr5g9N5/sZC/nxjIc/fWMh5g9M4WGPF5fGTlxrPFcOyuXPCAH40JJ0Mgw6f38/+Giv/2VbBwm8OsXZfDXXW0Pxua7BGb+3u0xWKvrJTQbfb7Q4uI/3888+Dm8nz8/OpqKg42VvFKSgUCkYdXWK+srgqvI0RHXL8iOCGQ/VUmhxoVUouK8gM7gE5nkqlYGQvDbhXr17Niy++GHx+uiOSw4YNY8uWLQCMGzeOZ599lm+++YbHH3+cgQMHdnXzg7qiL6ytrcXr9ZKZmdns+Kmytzc2NhIfH49Wq2XKlCm8/PLLzUZ5L7/8ct566y1WrVrFM888w1dffcUVV1xx0n1Ic+fObVaq48REedEi3aBDpVIEZ7t3VgaCbovDg93ds2cyo1212YHbE5rEQUca7BSXt72kc1J+Jj5fhBTxDZFo7iubvPLKK+Tl5RETE8O4ceP4/vvv2zx3/vz5XHDBBSQnJ5OcnMykSZNaPX/nzp1cffXVJCYmEhcXxznnnENJSUmH2iXCz+72crDGGsyrEyn8+Pl2fy2r99bgB87KTmDKiGw8vkBm9d0VZiqNDnZXmClrsLeozx2jUTEiN4mbxvTj5nP6MbJvEnqNCpvby+YjRhZ/X8IT/ynmy13VWJxdu/y8tMGOt4f3i60JRV/ZqaD7rLPOYt68eXz99desXLmSyy+/HIDy8nJSU1M71RBxTNO+7i92VeP2RljPIVrVYHNhcwZu5ssbbWw4VA/ARWdmYIhpmRhNpQoMriT3woA7FP7v//4P39Hfso8//jgHDx7kggsu4L///S8vvfRSyD43nH2hwWCgqKiIDRs28OSTTzJr1qxm+4x+8pOfcPXVVzN8+HCuvfZaPv74YzZs2HDSvUizZ8+msbEx+OiqepfdTaNSkhKn49wBqagUCqpMjuBMQE0EJtXpjJ5YQ9zl8YZsNUKd1clHW8v518ZSLi1ofUnnlcN7dhI1iP6+sqP5L1avXs3NN9/Ml19+ybp16+jbty+XXXYZZWVlwXP279/P+eefT35+PqtXr2br1q384Q9/CHm1DdG1nG4vB2osRNpts9fn4/PiKjaWBMrGjstL4aL8jKOZyQM8PnC4fe0qD5YWr+OCwencMSGPKcOzGZgWhxIFh+psLPq+hN8u3cK8r/azvayxSwYRI6F2dziEoq/sVPbyZ555huuuu44//elP3H777RQWFgLw4YcftpltV7TfwPR4DDo1ZoeH7w7Uc/5JMl6L8PP5/FQeneU+vjxYfqaBM46WXDieUgmj+iaRFCsBd1eZPHly8M+DBw9m165d1NfXk5yc3Ooqg67SFX1hWloaKpWKqqrmK1tOlb1dqVQG61OOHDmSnTt3MnfuXCZOnNjq+QMHDiQtLY19+/ZxySWXtHrOiXXWj6/BHm2yEmKoNTsZkZsIe2FnhYnzB6fTYHeR7Y1BI+XDIk5FoyMkM1Rmh5sPt5QfnT3yc6gmsKRzyogc7C5vsE53bygXFs19JTTPfwEwb948/vOf//DGG2/w0EMPtTh/0aJFzZ6/9tprvP/++6xatYrp06cD8PDDD3PllVfy7LPPBs8bNGhQh7+jCB+318uBGmvIVsl0lsvj5b/bKzjSYEcBXJyfSUF258phnUilUDIwLZ6BafHY3R5Scvvyzf5aShvs/HC4gR8ON5Ck1zB+UCoTBqVxOpFEb1whFoq+slN3HRMnTqS2tpba2tpmiX7uvvtu5s2b16mGiGNUSkVwtntlcdvLS0VkqLO4gkuBvtpTg8nhISFGzY/OSG9xrkoJg9LjJeDuQm63G7Vazfbt25sdT0lJCelNJHRNX6jVahk9enSz7O0+n49Vq1Z1qJ5kU33KtpSWllJXV0d2dna7rxnN0uK1KJUw4egS812VZrx+H34f1Fkjq2arAJvLQ4O16/cO2t0e/r2lHIvTQ0qslqtG5ASXdNZZnCToNWhUyh4/ww3R31eebv4LAJvNhtvtJiUlkGfF5/Pxn//8hzPOOIPJkyeTkZHBuHHjWL58efu/nAgrr9fHgRpriyXZ4WZ1unl/UylHGuxolEqmjsjpsoD7RHqNmksLMplzVQGPTCng4vwM4rQqjHY3n2yv5P/+vZ1/bTzC9vJGnJ7eF0B3VKj6yk4F3Xa7HafTGcyEe/jwYV588UV2795NRkbre6VEx4zsG/jZflZcFawNJyKPx+ujyhyY5d5TZWJ3lRkFMPmsLLQnzJqolIFVDHG6Ti0wEW3QaDT069cvLPVlu6ovnDVrFvPnz+fNN99k586d/OpXv8JqtQZnc6ZPn87s2bOD58+dO5eVK1dy4MABdu7cyZ///GfefvttfvrTnwJgsVj43e9+x/r16zl06BCrVq3immuuYfDgwc1Gb3sytUpJapyO4X0SidWosLu9HK4NZLOtszh7/N7daFNu7PrSPm6vl4+3VtBgc2HQqbl6ZA4xxwXXve2vQLT3lZ3Nf3G8//3f/yUnJycYuFdXV2OxWHj66ae5/PLL+eyzz7juuuu4/vrr+eqrr9q8Tk9JOhntfD4/B+us2ENQ7eB01FudLN1YSq3VhV6j4vpRfeifGhfyz1UoFPRLjeWWsf147seF/OpHgxjRJxGlAipMDr7cXc0b3xzi0+JKjtTb8NPLOsF2ClVf2amg+5prruGtt94CwGg0Mm7cOP785z9z7bXX8uqrr3ZpA3urghwDeo2KikYHO8qjd4lnT1dtduLx+jE53Hx5tDzY2LyW5cFUShiUIQF3qDz88MP8/ve/p76+vls/t6v6wptuuonnnnuORx55hJEjR1JUVMSKFSuCN5clJSXNkg1ZrVbuvfdezjrrLCZMmMD777/PO++8w89//nMAVCoVW7du5eqrr+aMM87grrvuYvTo0Xz99det1lLvqTITYlApFeRnB7Z57KwI9KVujx+jXWa7I4XR7sLi6NqbG6/fxyfbK6k0OYhRK7m6MAeDrmV+jd4m2vvK0/H000+zZMkSPvjgg+B+7aY9m9dccw0PPPAAI0eO5KGHHuKqq6466Qx8T0k6Gc38fj+H66xd3necrnKjjX9tKsXs9JCk1/Dj0X3JSOj+/AAalZLR/ZP5zSVDeHbaCCYMSiMlVovH52NPlZnlW8p489tDrD9QR+Mpfh8qQ7sQJiKFoq/sVASwadMmXnjhBQD+9a9/kZmZyebNm3n//fd55JFH+NWvftVlDeyttGoVF56Rxqc7qvhsRyXD+iSGu0niBC6PlxqLM1AebEcVLq+P7IQYxuQ1Lw/WFHDHaiXgDpW//vWv7Nu3j5ycHPr3709cXPMR5U2bNoXkc7uyL5w5cyYzZ85s9bUTk5898cQTPPHEE21eS6/X8+mnn7b7s3uqdIMOlVLB0KwENpUYOVhnxebyEKtVU2N2khLXewYgIpXP56eioWuT9Pjx8/nOag7X21ArlUwtzJH/10dFc1/Z2fwXAM899xxPP/00n3/+OSNGjGh2TbVa3SJoHjp0KGvXrm3zerNnz2bWrFnB52VlZRJ4d7Mj9fZgmdZIsa/Gwmc7KvH6/WQlxHDViGz0mvDf+yXFahncL5lR/ZKoMjnYVWFmT7UZs9PDhsP1bDhcT06inqHZCQzOiAsm59SplaQn6EjUqzHZ3cH8F71hO04o+spO/U2w2WzB2mWfffYZ119/PUqlknPPPZfDhw935pKiFZcVZAWC7uIqZl12ZribI05Q0ejA74ONh+upMNnRqpRcWpDVLCOlWqVgYHqcBNwhdu2114blc6UvjGwqpYLUeC2+OB1ZCTFUmhzsqjRzdr9k7C4fFqebeJn9DKs6q6tL92L68bN2by17qswoUXDlsKwWK496s2juK4/Pf9H0PZryX7Q1YAnw7LPP8uSTT/Lpp58yZsyYFtc855xzWtTd3bNnD/3792/zmj0p6WQ0KjfaqY+w3BxbShv4em8tfmBgahyXnZWJJsIqSyhQkJWgJytBzwVD0thfY2VnpYkj9TbKG+2UN9pZs0fJ4Iw4zumfwrhBqazYXsGqXdXYXF5itSom5Wdy5fCsHp94MhR9ZacigcGDB7N8+XKuu+46Pv30Ux544AEgsDcmISE0SQJ6o4vzM1ApFeyqNHOk3kbflNhwN0kcZXN5MNrcVJrsfHcwsPTkR2ekk6g/dgMvAXf3mTNnTlg+V/rCyJeZEEMFUJCdQKXJwc4KE6P6JaFAQY3ZKUF3GHm8XV+KZlNJA0WlRgAuKcjoln2U0STa+8pZs2Zx++23M2bMGMaOHcuLL77YIv9Fnz59mDt3LhDImv7II4+wePFi8vLygnu/4+PjiY+PB+B3v/sdN910ExdeeCEXXXQRK1as4KOPPjppeUURPjUmJ9URVPrR5/fz/qZSPHtrARiWk8iPzkhvNgETiVRKJWccrbJjdrrZXWlmZ4UJo93NzkozlwzN5JPtFXy89djWNpvLy4dbywG4fFjPLrEYir6yU3u6H3nkEX7729+Sl5fH2LFjgxl2P/vsM0aNGtWlDezNkuO0jOl/LKGaiBwVjQ6cHi+f7QiUBzsjw8CZWcfKg6lVCllS3gtIXxj50uJ1KJUwJCMetVJJvc0VDPRMdg8uyeQaNlVHc2J0leIKE9/urwPg/MFp5GfKwFek6Kq+sqP5L1599VVcLhc33HAD2dnZwcdzzz0XPOe6665j3rx5PPvsswwfPjxYVuz888/vom8vukq91UlZCJIudpbb6+O1rw/y6Y7APfr4galMPDPyA+4TGXQaxvRP4afn9ueGs3MZ0y+ZguwEvthV3er5n++qQtkbN3qfpk5FBDfccAPnn38+FRUVwVqLAJdccgnXXXddlzVOwGVnZfHdwXpWFldy1/kDwt0cATTaXbg9PjYcqqfR4cagUzPxzHQUBDogtUrB4Iz4ZllyRWgplcqTlnEIVbZe6Qsjn0qpICFGg88Hg9Pj2FVlprjcRFaCHr8fai0ucpJk+XF3c7q91Fq6brbqQK2FL3YFbnzP7pfMqKMVQERzPaGv7Ej+i0OHDrXrmnfeeSd33nlnh9ohuleN2Ul5feQE3DaXh7+t3s+uSjMqhYJLh2aSnxXdA30KFGQn6hmQFofN7cXmar0/sLm82F1eNPpOzd1GhVD0lZ2ehsvKyiIrK4vS0lIAcnNzGTt2bGcvJ9pwWUEmf/y4mO8P1tNgdZEcJ/Wdw8nm9BCrVZGRoOPn5w+kuMKEye5Gd3Rvi0atYFC6BNzd7YMPPmj23O12s3nzZt58800ee+yxkH629IWRLylOg9HmZmh2AruqzOyttnDBkDQ0KhV1FieZBh0qVc+9eYhE5Y12/F20lbu80canOyrxA0OzDJw3KLVrLtwDSV8popHR5mJ7WSPJEVLhqsHm4sXP91JmtKNTK7l34iDyd9eFu1ldxuP1Ea9TEatVtRp4x2pV6LU9+z43FH1lp4Jun8/HE088wZ///GcsFgsABoOBBx98kIcffhilUm5eukrflFjyswzsqjTzxa5qpo3ODXeTei2Xx8tnxZXNEkpckp/B5cOyOVhjxYefwenxPXqPS6S65pprWhy74YYbOOuss3jvvfe46667QvK50hdGhwSdBqUS+iTrSYhRY3J42F9rJT8zAa8P6m1u0g2S3bq7WJxuGm1dk3W4zuLk460VeHx+8lLiuCg/I7jqSLQkfaWINmaHm6IjRry+yIi4y4x2Xvx8Dw02N4l6DfddPIR+qbGw+9TvjRYeHzTaPUzKzwzu4T7epPxMfD4/9ODb3VD0lZ0Kuh9++GFef/11nn76aSZMmADA2rVrefTRR3E4HDz55JOduaxow2UFmeyqNLOyuEqC7jCxOT18VlzJRycklPhoawUKFJw/JI14nVoC7ghz7rnncvfdd4fs+tIXRgelUkGiXkODNTDb/d3BenaWm4J7fmvNzqgLui0ON3VWFzanl1idinidOmr6oHJj1yRPq7M4+WZLOU5PoFzj5cMyUSkkeOsM6StFJLK7vBQdMXZp7ofTsbvSzCur92FzeclKjOH+S4aQFh9dvzvaq8bkZPKwQCm+z3dV9brs5W05nb6yU0H3m2++yWuvvcbVV18dPDZixAj69OnDvffeK51nF7u0IIuXvtjHV3tqcLi9snS5m1mdbrRqJatOklDiyhHZaGR5akSx2+289NJL9OnTJ2SfIX1h9EiMPRp0ZyXw/cF6So12Gu2BmQqnxxf8cyRze30YrW5qrU6c7mNrs50eHw1WNwBatZK4CA7CG44OFJwus8PNC5/v5SyXh5Q4LVeNyIm48jzRQvpKEYmcHi+bSxqa9XXhtOFQPa+vPYjHF1jVOPPiwcTrem6yXKfHx8EaKxMGp3HliCzsLl+wTndvDbhPt6/s1N+W+vp68vPzWxzPz8+nvr6+Uw0RbRvWJ4GsxBgqGx18s6+WS4ZmhrtJvYbV6aHMaCfDENOrE0pEuuTk5GYJL/x+P2azmdjYWN55552Qfa70hdEjQadBpQRDjIbc5FiONNjYVWli3IDA/t8aszNig+6mWW2j3X3KfdAujw9XG0G4IUaNNow3Sz6fn4rG05/ldri9/GXVXipNDs7Vqbm6MEcGo9tJ+koRDdxeH5tLjG3ed3W3zUca+PuRAwCM6pfEL84fiFbd8+/5nB4fpQ12zE41A9ICJfZ68pLy44Wir+xU0F1YWMhf//pXXnrppWbH//rXvzJixIh2X2fNmjX86U9/YuPGjVRUVPDBBx+ctBj5smXLePXVVykqKsLpdHLWWWfx6KOPMnny5M58jaihUCi4rCCTt9Yd5rMdVRJ0dxOr08P+GgtKAkkjenNCiUj3wgsvNOsclUol6enpjBs3juTk0GUx7qq+UISeUqkg4egS84JsA0cabOysMDF2QAoKFFgcHuxuL/oICd483kDgfOKsdkedMgjvqga3Q7XFictzerNWHq+PV1fv51CdjXidmmtG5mCQWuvtJn2liHQ+n5+tpUYsjq7J+3A6/PhZu7eWolIjDB7ExWdm8JNz+va6clkRsp2+W4Wir+xU0P3ss88yZcoUPv/882CtxXXr1nHkyBH++9//tvs6VquVwsJC7rzzTq6//vpTnr9mzRouvfRSnnrqKZKSkliwYAFTp07lu+++6/E1cS8ryOKtdYf5fGcVXp8fVS/7B9/drE4PB2os+HxQaXbg8cPF+Rl8fNye7ia9IaFEpPvZz34Wls/tqr5QdI/kWC0NVjcD0+LRqWswOz2U1tvpmxILBPaw9UuNDWsbOzKr3RknBuHWfbUkxWpIidOSHKsN2Yyx2+ul2nR6s9w+v583vjnEjgoTWrWS31w8mOTtrW/7Ea2TvlJEMr/fz7ayxmD/FE5en4/PiqvYVxNI/Dft7D5cflbWSctIiZ4jFH1lp9ZG/OhHP2LPnj1cd911GI1GjEYj119/PTt27ODtt99u93WuuOIKnnjiiXbXaHzxxRf5n//5H8455xyGDBnCU089xZAhQ/joo4868zWiyriBKRhi1NRZXRQdaQh3c3o0mysQcHt9gX2DH2+t4F8/lHJpQSZTR2QTe3RWO1ar4uoROVw5PCvi9k32NgsWLGDp0qUtji9dupQ333wzZJ/bVX2h6B7xOjUqJahVSs7INABQXGEKvt5gd+H2dv/+QY/XR43Jyc4KE/uqrTRYQxNwt8bu8lJhdLCjzMTavbV8s6+WHeWNVDTacbi7bmlnZaMT32l8J7/fzz9/OML3h+pRKRTc+6NBDEyP77L29RbSV4pIVlxhosbsDHczcLi9/HtLOftqLCgVCiYXZHLFsOxeG3DH9eC9620JRV/Z6Q0JOTk5PPnkk7z//vu8//77PPHEEzQ0NPD666939pId5vP5MJvNpKSktHmO0+nEZDIFH2azudva15U0KiUXnZkBwGc7qsLcmp7L5vKwvzoQcDs9Xj7cWo7V5cHl9bK/2soFQ9J5/saRvHDjSJ6/cSSXD+u9GRwjydy5c0lLS2txPCMjg6eeeiqknx0JfaFon0AW88CC6oKsQOby/TWWYHDp90Gd1dVt7THaXByus7KjwkSZ0R4RCYNODMKLy02U1NmotzpxeToXhNvdXupP8+f6yfZKPt8ZmNW+Y0Iew/okntb1eivpK0WkKjfaqeiiyganw+x08/7mUsqMdrQqJVcX5nDG0UoXvY1CCX1T9GQlxIS7KS3MnTuXc845B4PBQEZGBtdeey27dzev2+ZwOJgxYwapqanEx8czbdo0qqraF0OFoq+M6iwAzz33HBaLhRtvvLHNc+bOnUtiYmLwUVBQ0I0t7FqXnRXYy/3pjkr8/l64wSLEAjPcVry+wLKi/26roN7qIk6rZmphDn5/4KZdo1KSoNegUSllhjtClJSUMGDAgBbH+/fvT0lJSRhaJCJVUmxg/296go7UOC1ev5+91ccGY+sszsB2kRBxe32U1Nn4dn8tPxxq6NZZ7c5weXzUW12U1NkpLjezsxNBeLnRzun8yvp6bw3LNpcBcOOYXM4dmNr5i/Vy0leKSFRtclBtCv8Md63Fyb82lgbv/aadnUvf5PBuOQoXlRIGpsWRGqEl0b766itmzJjB+vXrWblyJW63m8suuwyr1Ro854EHHuCjjz5i6dKlfPXVV5SXl7drOzOEpq+M2vUCixcv5rHHHuPf//43GRkZbZ43e/ZsZs2aFXxeVlYWtYH3j85IR61ScKjOxv4aC4PD3aAexO72cqDGisfrx4+fL3dXU2q0o1EqmToikKgnPUFHcmx3ph0S7ZWRkcHWrVvJy8trdnzLli2kpsoNujjGEKNGrVLg8cLQ7ATW7quluMLE8D5JALg9fox2FylxXXujYbS5KG2wU212nNYy63Bzenw4PS7qj97X6NRK4o4mZYvTqVpkRzfZ3ZjtnU+IVHTEyFvrDwNw+VlZXFaQ1elrCekrReSpszgpj4AZ7tIGG//ZVoHL6yMlVsvVhTkYYnpnkkadWsnA9LiInlhasWJFs+cLFy4kIyODjRs3cuGFF9LY2Mjrr7/O4sWLufjii4HAkvGhQ4eyfv16zj333JNePxR9ZVQG3UuWLOHnP/85S5cuZdKkSSc9V6fTodMdu3kymUwnOTuyGWI0TBiUxld7avisuEqC7i5id3vZX23B4w1MxWw4WM/OSjMK4IphWaQbdMTpVGRH4PIaEXDzzTfzm9/8BoPBwIUXXggERkHvu+8+fvKTn4S5db1AuPNqTJ3a7lMVCgUJMRrqrS7yswx8u6+OarOTWouTtKMj+jVmZ5cE3R6vj/I6G2VGO1Zn+DPxhsKxIDywfPzEILzcaO/0tcuMNv6+Zj9+P0wYlMq0s0NXR7q3kL5SRBKjPTAYGW57qkys3FmNz+8nJ1HPlOHZvbYMYXyMirzUONSq6FoM3djYCBDccrxx40bcbnezODE/P59+/fqxbt26UwbdoegrOxR0n2pK3mg0dqoRHfHuu+9y5513smTJEqZMmRLyz4s0lxZk8tWeGlbuqOLefuFuTfRznBBw76o08d2hQM3QiWdm0D81DrVKQf/U2F5XIiKa/PGPf+TQoUNccsklqNWBbs3n8zF9+vSQ7FOMhL5QdF5SbCDo1mvUDEiPZX+NlZ0VJi4Ykg6A3eXD4nQT38lSVBanmzpLIAN5TVV05hHprBOD8M6qtTj5eGsF7gG5FOYmMn18Xq9NYtSVpK8UkcLicHO4znZaW09Olx8/m0uMfLO/FoDB6fFcVpCJShldAWdXSY7T0Dc5vPe7ZrO52QTpiZOnrfH5fNx///1MmDCBYcOGAVBZWYlWqyUpKanZuZmZmVRWVp6yHaHoKzsUdCcmnjxxSWJiItOnT2/39SwWC/v27Qs+P3jwIEVFRaSkpNCvXz9mz55NWVkZb731FhBYUn777bfzl7/8hXHjxgV/aHq9/pRt6ykuLcjk/5ZvZ/MRI8Y0F0my3LnTnG4v+2uOBdxHGmys2hVI1HN2v2SG5SSiUEBeamyLJZMismi1Wt577z2eeOIJioqK0Ov1DB8+nP79+4fk87q6LxTd69gScz9DsxPYX2NlV6WZ8walBm+2aszODgXdTXW166xOHBGQEC2amRxuPtxSjsvrY3B6PHdfOFDKZHYR6StFJLC5PByotYY1n4Xf7+frfbVsKTUCMDI3ifOHpKGgd/Y12UkxZEbAis4TtwDPmTOHRx999KTvmTFjBtu3b2ft2rVd1o5Q9JUdCroXLFjQ6Q9qzQ8//MBFF10UfN609/r2229n4cKFVFRUNNus/o9//AOPx8OMGTOYMWNG8HjT+b1BZkIMhbmJbCltZMuRRn50Znq4mxSVnG4v+2osuD2BgLve6uS/2yrw+f0MyYjnvEGB/RpZiTHE99I9PdFoyJAhDBkyJOSf09V9oeheCoWCRL2GOouL/ilxxGnVWF0eDtXZGHS0DJXJ7sHl8Z5ywK1pVrvR7o7qvdqRwu728O+iMqwuDylxWq6+eDA6GfTsctJXinBxHs2h0539pfpoqUiP14fH17IG9/mD0xjVN7n7GhRBlErolxIbMZN4xcXF9OlzbCvRqWa5Z86cyccff8yaNWvIzc0NHs/KysLlcmE0GpvNdldVVZGV1f7cIF3ZV4Z1/cTEiRPx+/0tHk0B9MKFC1m9enXw/NWrV5/0/N7isrMCf1k2S73uTrG5PM0CbpvLE5xVyU7Qc+nQTBQoSNSrI2LUT5zatGnTeOaZZ1ocf/bZZ/nxj38chhaJSJd8NIu5UqHgzKyWNbv9fqi1tL5E2uP1UWN2sqvCxL6qQF1tCbhPn8vr5cMt5Rjtbgw6NdcU5vTK+rChJH2lCCeXx8v+o0lru4NOrSQ3Rc+Z2QayEmM4M9tATlIM3+yvY1+NBZVCweSCrF4bcGvUCgZnxEdMwA1gMBhISEgIPtoKuv1+PzNnzuSDDz7giy++aJFpfPTo0Wg0GlatWhU8tnv3bkpKShg/fvwp2xGKvrJ3blqIcpcVBEqH7awwYXd3rm5qb2V3edl02BgMuN1eLx9tLcfs9JCk1zBlRBYqpRKdWkm/lN5ZJiIarVmzhiuvvLLF8SuuuII1a9aEoUUi0sXpAkvMAQqyA0H34TorVqc7eE6dxYnXeyyatjjdHK6zUlxhoqzBLsvIu1BTmcZqs5MYtYprRuZ0ek+9aJv0lSJcPF4fB2qtuDzd02/q1EoGpMexdm8Ns/65hQeXbmHWP7fwzf5afnZeHv1SYo/W4DZ0S3siTYxWyZCMeGK10TmwOWPGDN555x0WL16MwWCgsrKSyspK7PZAYr7ExETuuusuZs2axZdffsnGjRu54447GD9+/CmTqEFo+koJuqPQ4Ix4+qfG4vH52VHWGO7mRA2H28vGww04jg5U+P1+Pt1RFbzJmzoiB71GjVIJ/dNiUUVZ5sbezGKxoNW2HKnVaDRRXbFAhI5CoQiO7ifH6shOiMEP7Dou8ZnXBzVWl8xqh5gfP5/vrOZIw9EyjYXZJMdGZm3YUJs7dy7nnHMOBoOBjIwMrr32Wnbv3t3sHIfDwYwZM0hNTSU+Pp5p06ZRVVXVrutLXynCwevzc7DWisPVfZ1neoKOFdsr+GhrBTZX4L7P5vLy8dYKVu2q4u4LBpLbS2twJ+jVDMkwRHW+oldffZXGxkYmTpxIdnZ28PHee+8Fz3nhhRe46qqrmDZtGhdeeCFZWVksW7asXdcPRV8pUUUUUigUwdnuzSWyxLw9WgTc+Fmzt5aDdVZUCgVTC7ODN+B9k2OjduSvtxo+fHizjrbJkiVLWiTlEKJJUuyxf+dDsxMA2Fluxs+xpY+VRofMaodQU1+8p9qMEgVXDs8iK0Ef7maFzVdffcWMGTNYv349K1euxO12c9lll2G1WoPnPPDAA3z00UcsXbqUr776ivLy8lNmCW8ifaXobn6//+gqou5bmalWQqJeHUyOe6IvdlWTZtCi7oVRUEaCjgFpcVGfnLK17cZ+v5+f/exnwXNiYmJ45ZVXqK+vx2q1smzZsnbv5w5FXymRRZS67Kws5gFbSxvxeH1RV0+vOzUF3HbXsQ6/6IiRrWVGFMBlZ2UGb/LSDFqS4yJnb4tonz/84Q9cf/317N+/n4svvhiAVatWsXjxYv71r3+FuXUiUsXrNGjUCtyeQALFNXtrabC7qDI5enXg1502Hm5g69HswZcWZNAvJS68DQqzFStWNHu+cOFCMjIy2LhxIxdeeCGNjY28/vrrLF68ONjXLViwgKFDh7J+/fpTLpuUvlJ0t5J6Gya7p1s/U61SYnV6gzPcJ7K5vFhd3kBytV6ydEmhgNxkPanxvXMVUUeFoq+USC1Knd0vGYNOjc3tZU+VJdzNiVgOt5dNJwTcGw838M2+QE3G8walMTg9sJ8nVqciJ1FutKPR1KlTWb58Ofv27ePee+/lwQcfpKysjC+++ILBgweHu3kigjWtcNGqVQxODwR8xeWyzLY77ChvZN2BOgAuHJLGGZkJYW5R5GlsDGwhS0lJAWDjxo243W4mTZoUPCc/P59+/fqxbt26U15P+krRncqMdhqs7lOf2MU8Xh+xWhWx2taXT8dqVcRpVXi8vSPgVqsUDEyPk4C7A0LRV0rQHaVUSgWFfZOAwKytaKkp4D5+pHN/jYXX1h7ADwzvk8iofklAoEPKS41FGeXLbXqzKVOm8M0332C1Wjlw4AA33ngjv/3tbyksLAx300QES9IfS9ZVcHSJ+d5qC26vJKkMpQM1Fr7cHVj6OaZfMoW5PTt7sNlsxmQyBR9Op/OU7/H5fNx///1MmDCBYcOGAVBZWYlWq21WAgcgMzOTysrKdrVF+krRHSpNDmpMp/573tW8Ph+r99Swo9zExfkZrZ4zKT+TRpuHbsrpFlaxWhXn5KVgkPK3HdbVfaUE3VFs5NESB5uPNOD3d0/5hWjh9HjZVNI84K42O3j5i324vX7yUuO4cEg6ChQoFIEahdGcUEIErFmzhttvv52cnBz+/Oc/c/HFF7N+/fpwN0tEsDidGu3RjX05yXoS9RpcXh/7a6yneKforDKjjRU7KvETGOg4d1BquJsUcgUFBSQmJgYfc+fOPeV7ZsyYwfbt21myZEmXt0f6ShFKdRYnlUZHt39ujdnJkg1H2HzEyPsby7i0IJOpI7KDM96xWhVXj8hh8rAsaszdPyDQ3ZJiNYzJS5HSi6ehK/tK+b8QxQqyDWhVSuqtLkrqbfRP7d174Zq4vYE93LbjknZYHB7+smovFqeHfimxTM7JRKkIzGpnJsaQoJcRwGhVWVnJwoULef311zGZTNx44404nU6WL18uiYFEuyTFaqg2OVGgYGiWgfUH6ymuMJGfJcudu1qtxcnHWyvw+v0MTI3jojMzUNDzVxgVFxfTp0+f4PO2as82mTlzJh9//DFr1qwhNzc3eDwrKwuXy4XRaGw2211VVXXKBEHSV4ruYLS5KG2wd+tn+vx+Nh6u5/uDDfjwE6tRMToviSN1ds4fnM6UEdlYXV7itCqMNg8Ha6w4e/g0d1ZiDAXZCbKCsxNC1VfKTHcU02lUnJUTuCksKjGGtzERwu31sb/a2izgdnt9vLJ6H1UmJylxWn5z8WC0qsCoZ4JeTVZCTLiaK07T1KlTOfPMM9m6dSsvvvgi5eXlvPzyy+FulogyiccNuuVnJ6AgsBex0e4KX6N6oEa7mw+3lOPy+shJ1DP5rKzg4GdPZzAYSEhICD7aCrr9fj8zZ87kgw8+4IsvvmDAgAHNXh89ejQajYZVq1YFj+3evZuSkhLGjx/f5udLXym6g9nh5nC9je5cfGm0uXh/UynrD9bjw8+g9DhuGdePAanxOD0+Shvs7K4wU2l0sLvCTFmDvccH3IMy4hnWJ1EC7k4IZV8pQXeUG3l0X/fmI1I6zO31sb/G0qy0j8/vZ8E3h9hbbUGvUXHfJUOOS5ykpF9K76zR2J1CWXf2k08+4a677uKxxx5jypQpqFSyRUB03PFLzA06DX2P1m7dWWE+2dtEB9hcHv69pQyry0NqnJYpw7Ol6kYrZsyYwTvvvMPixYsxGAxUVlZSWVmJ3R6YOUxMTOSuu+5i1qxZfPnll2zcuJE77riD8ePHnzRzufSVItSsTg8Ha634uyme9eNnW6mRdzccodLkQKtScmlBJlcMy0avab6Q1+MDh9vX4/dwq5QKhucmMiBNVr52Vij7SvmNF+UK+yahAI402HvF/pS2eJoCblfzHnX55jK+P1SPSqHg3omD6JMUyE6uVEJeWqzc9HWDUNadXbt2LWazmdGjRzNu3Dj++te/UltbG8qvI3qopNhjs91NNbt3VZpQKiBGo+yV9VxPl1oZ+Nn5/X4+2lpOo92NQafmmsIcYjQS9LXm1VdfpbGxkYkTJ5KdnR18HF8v9oUXXuCqq65i2rRpXHjhhWRlZbFs2bKTXlf6ShFKRruL/TUWuqv6lsUZWDWzem8NHp+P3CQ9t4zrR35mQq/YrtIarVrJ2f2TyZTVm6cllH2l3EZEOUOMhiGZgZJXW3ppFvO2Au6v9tTw3+2BbK7Tz+sfvJEG6JOkJ1YrKQ26w4oVK/jZz37GWWedRWFhIQsXLqSkpISNGzcCBOvOPv/881x88cWMHj2aBQsW8O23354yWcW5557L/Pnzqaio4Je//CVLliwhJycHn8/HypUrMZtlplK0z/FB96D0OPqnxHL7eXnkZxvISozhzGwDucl6dBJ9n5JOrSQ3Rc+Z2QYyE2MoyEngxjF9yUuJ45qRfYjTSQ6Ntvj9/lYfP/vZz4LnxMTE8Morr1BfX4/VamXZsmWn3M8tfaUIlRqzk8O1tm4LuPdUmVn8fQkl9TbUSgUXDknj2lF9MPTifiVGq2TsgJRmW6VE54Syr5S7hx5gVNMS85Let8S8KeC2nxBwby9rZNF3hwGYOiKbCYPSgq/lJOmlVmEX6UwZnK6uOwsQFxfHnXfeydq1a9m2bRsPPvggTz/9NBkZGVx99dWd+Gait4nVqoMBdaxWzazLzuBQnZUHl27hwaVbmPXPLXyzr5YB6XESeJ+ETq1kQHoca/fWMOufW/jt0i389l9bAj/LyWdIDo0wk75SdKUyo52yBnu37OG2OD2s2FHBp8WVOD0+Mgw6fnJOXwpzk3vt7DYEchMNSY+X1UNdLBR9pdw59ABN+7r3VFmwON3hbUw38nh9HKi1tgi4ayxOXv1qPz4/jB+YytWFOcHXEvQa8rMM3d3UHqujZXBCVXf2eGeeeSbPPvsspaWlvPvuux1+v+i9Eo/Odqcn6Pi8uIqPt1YEyw7aXF4+3FrOp9srSTfIoF1rvD4fyXEaVmyv4KMTfnYfb61g5Y4q+dlFEOkrRWf5fH4O1lq6rQ739rJG5ny4g73VFhTAuLwUbhidS3Js7+5P0hN0DEiLQyVbJUOqq/pKWV/bA2QkxNAnSU+Z0c7W0kbOO25Wt6fyHg24j89SDmB2uvloSznOftnkZxm4fXx/FEez46pVCkbkSjbHrtTRMjhNdWfXrl0b6qahUqm49tprufbaa0P+WaJnSIrVUG9xkqhXs2pXdavnfL6riiuGZ9FQ5kStUhKrUaFShueGR60EtUqJxxv6BEFurxeL04vF6cHq9GBxerA4jv736EOtVPDMtBEn/dldOSKLykZ6fEKjaCJ9pegIt9fHoVor1hPuv0LB6fbyz42lfLWnBoDkWC2XDc0ko5evmFEooE+ynjRZtdmtTrevlKC7hxjVL4kyo52iEmOPD7rbCrhdHi8fbynH6vKQkxjDvRMHBROlKRQwrE+iLL/pYk1lcNojFHVnhehKsVo1sTo1Vqc3OEt7IpvLi8nh4cvdNZQZAxmldWolsRo1sToVeo0qcB2tklitmoojRhJi1CToNSTEaIJZ0k+HTq0kPUFHoj7Q1jidikabhxqzs1OlcOwuLw02V+BhdQf/XG9zUfDDYSxOT7uu2ydJj8nhOenPzuryBgYKumsDqBCiyzjdXg50U43r/TUWXlt7MJgkeNLQDH6S07fXJ8BVKaF/ahwJsn876kjQ3UOM7JvEx1sr2FbWiMvjRavumcFlU8B94gir1+/jk+2V1FpdxGnV3HfJkGaJ0vLS4mREMEz8fj+//vWv+eCDD1i9evVJ685OmzYNaF/dWSFCIVanIk6nIlarajV4jNWqMOjUeH0+lAoFPr8fp8eH0+Oiwd7yequ8+5o9j9EoSYgJBOAJejUX7q4mVqtCr20K1lXB59pWSpU07Zlesb2CVbuqsbm8xGpVTMrPZPKwLA4ed0Psx4/T7Ts6E+0OzFQ7mv7sweL08lHFppPeQGdaj9Uq1yiVxMeoidepiNepiddpjnuuIUmvISlWfdKfXZxWhccb/oBboQBNL795F6IjmkqCebyh3cDt8fr4cGs5n2yvxO+H5FgNd04YwNDsBNRf7grpZ0c6rVrJwPQ4mUCKUhJ09xB5qXEk6TUY7W52VpopzE0Kd5O6nNfn52Bdy4Dbj5+vdtdQ0mBDrVRy1fBslMcF2KnxWgZKzcKwmTFjBosXL+bf//53sO4sBOrN6vX6ZnVnU1JSSEhI4Ne//vUp684KEQqJei2Ndg+T8jP5cGt5i9cn5Wdidnj48eh+waDW5grM7tpcXuzuY3+2ubzkpcZicngw2d14fH4cbh8Ot5Pqo7M3yeWNbbZFrVQGZ8xjtYFZ9GtG9QnumW7StN/cj5+h2Qks21waXAbu8Z38Brkp4I7VqkiO1ZIcqwn8Ny7w57NjjxwNrAO1zE+VsOhUP7tGmyesS8vVKgUpcVrS4rU9dnBaiK5mtLsoqQt9hvKyBjuvrT3AkaMjmOMHpnLz2L5SbQaI06nIS4uTwcIoJn+LewiFQsGofsl8ubuaopKGHhd0e31+DtZasThazp5sPNzAjgoTCmDyWYG9Pk0V9WI0Kob1SQzu6xbd79VXXwVg4sSJzY4vWLAgWAbnhRdeQKlUMm3aNJxOJ5MnT+Zvf/tbN7dUCNBrVFQY7UweFtja8PmuqlZnkwEUKIjRqIjRqEhpY1xv/EUFQGDFh93tDQbgJocbk91DP89ebE4PdndToO7B5vLh8QUeJocPk8MDgEGnJjdJz4uf72n1s1btquaKYdmY7G7MTk+z7xSYmT76OBpEx+nUXDRpGMl6Dbo2Zk7SSjs2YFljcrbrZ9fd4nQq0uJ1JOo1ktdDiA6oMTspN4Y2Q7nf72fzESNPlRXj8fmJ16m57dz+jO6fHLoPjSLJcRr6JsdK3xXlJOjuQUb2TQoE3UeM3Obz95h/nMcCbk+L1/ZUmVh3oA6AC4ekMzAtPviaUgnDcxNlVDDM/O34Td1Ud/aVV17phhZFpldeeYU//elPVFZWUlhYyMsvv8zYsWNbPXfZsmU89dRT7Nu3D7fbzZAhQ3jwwQe57bbbguf4/X7mzJnD/PnzMRqNTJgwgVdffZUhQ4Z011eKWrFaFQdrrEwYnMaVI7KwurzEaVUYbZ5my7c7QqFQHJ2xVjcrm5VWkdriXD9+3F4fNqcXmzsQhNtdHnRqJRbnqfZMe7h8WBZenz+w/Ft38kRv+i5OSOT0+Lr8Z9dZSiWkxGlJjdehl+WYQnRYudFOdYgzlDfa3azaVUWZ0Y5n8CBG5CZy+/g8qTl9VFZSjJRa7CEk6O5BzswyEKNWYXJ4OFBrZXBG/KnfFOF8Pj+H6loPuMuMNj7fGciSO7JvEiNOmN0/MytBOm0RFd577z1mzZrFvHnzGDduHC+++CKTJ09m9+7dZGRktDg/JSWFhx9+mPz8fLRaLR9//DF33HEHGRkZTJ48GYBnn32Wl156iTfffJMBAwbwhz/8gcmTJ1NcXExMjPwCP5mkWC2VjU5KG+xUNnZfhvAmChRoVSq0sSqS0AaPq5WBmqwn3W8eE5gND+cSbqfHF7afHQT2zafG60iJ1UgpHSE6wefzU1Jvw2gLXRlaP36Ky018vbcWt8+HRqnk9vH9OX9wmqxOBBRK6JcSS3Ks9tQni6ggv416EI1KyYjcRACKjhjD25gu4Du6h9tsbxlwN9ic/GdbBV6/n8Hp8Zw/uHnG9uykQBk1IaLB888/zy9+8QvuuOMOCgoKmDdvHrGxsbzxxhutnj9x4kSuu+46hg4dyqBBg7jvvvsYMWJEsBSb3+/nxRdf5P/+7/+45pprGDFiBG+99Rbl5eUsX768G79ZdIrRqIjRBn49enzgcHdv0NgWj+/YnunWRMKe6eN1589OoQyUfBucEUd+dgLpBp0E3EJ0gsfrY3+NJaQBt83l4T9bK/hidzVun4+cRD03j+3HBUPSJeAmkHticHq8BNw9jPxG6mFG9ksCYFNJQ3gbcpqaZrhbC7htLg8fbinH6fGRlRDDpUMzmyX3idEqyc9qXxkrIcLN5XKxceNGJk2aFDymVCqZNGkS69atO+X7/X4/q1atYvfu3Vx44YUAHDx4kMrKymbXTExMZNy4ce26pgjMdkeipj3TV4/IIVYbWDIdq1Vx9YgcJg/LCpbX6S00agVZiToKsg3kpcURHyOrm3q6V155hby8PGJiYhg3bhzff/99m+fOnz+fCy64gOTkZJKTk5k0adJJz7/nnntQKBS8+OKLIWh55HO6veyttoS0Bve+GguLvivhYJ0VlULBhEFpXD+qj6xMPCpGq+SMzHjidLIYuaeRoLuHGd4nEZVCQZXJQbmxlfo1UcB7NOA2tRJwu71ePt5WgcnhITFGw1UjspvVbFQpYUBqHKoesp9d9Hy1tbV4vV4yM5vPXmZmZgYzvbemsbGR+Ph4tFotU6ZM4eWXX+bSSy8FCL6vo9d0Op2YTKbgw2w2d/ZrRb3kCL0BPH7P9PM3FvLnGwt5/sZCzhuc1u17psMpJV7LiL6JFGQnkJWoR9NKeTXR8zRtxZkzZw6bNm2isLCQyZMnU11d3er5q1ev5uabb+bLL79k3bp19O3bl8suu4yysrIW537wwQesX7+enJycUH+NiNRod7O32oLTHZo+xOnxsrK4kk+2V+DweEmL03LjmL6c3S9ZZrePMujVDEmPl8oK7bBmzRqmTp1KTk4OCoWixSo+v9/PI488QnZ2Nnq9nkmTJrF3797wNPYoCbp7mFitmvwsAxCdS8xNdje7K82tBtx+v5/PdlZRZXIQo1YytTAHvab5SGC/1Lg2s/AK0ZMYDAaKiorYsGEDTz75JLNmzWL16tWndc25c+eSmJgYfBQUFHRNY6OQTqNCr43MX5FNe6Z3V5ipNDrYXWGmrMHe4wNutUpBv9RYzhucytn9kskwxMjNei/T0a04ixYt4t5772XkyJHk5+fz2muv4fP5WLVqVbPzysrK+PWvf82iRYvQaCJzwC2UasxONh1uCFkN7iMNNt79voRdVWYUwJh+ydw4pi9px5V37e3SDDoGpsXJtph2slqtFBYWtpmAtymvzbx58/juu++Ii4tj8uTJOByObm7pMbJ2oQca1S+ZHRUmNh9p4Mrh2eFuTru4vT7KjXYarG3vIVq7r5YDNYHlSFcOz26x1yUjQSfLk0TUSUtLQ6VSUVVV1ex4VVUVWVlZbb5PqVQyePBgAEaOHMnOnTuZO3cuEydODL6vqqqK7OxjfUBVVRUjR45s85qzZ89m1qxZwedlZWW9OvBOitVid4XvF/SpeHzgCXXh3AhgiFGTmxJLVkKMrGLqxZq24syePTt4rCNbcQBsNhtut5uUlJTgMZ/Px2233cbvfvc7zjrrrC5vd6QrbbCxu9IckpJgHq+PdQfqKCo1ApCo13Dp0EyyEyXnDgQGElPitKTGaWXCqIOuuOIKrrjiilZfOzGvDcBbb71FZmYmy5cv5yc/+Ul3NjVIhlN6oJF9kwA4WGPFaHeFtzHt0GB1sbvSfNKAe0tpQ7DTnjQ0kz5Jsc1eN+jVZCdKRmYRfbRaLaNHj24289I0EzN+/Ph2X8fn8+F0BvbzDhgwgKysrGbXNJlMfPfddye9pk6nIyEhIfgwGAyd+EY9R5IM4oWNQhmoTXtOXgrjBqbSJ0kvAXcv19mtOMf73//9X3Jycprlu3jmmWdQq9X85je/aXdbespWnH3VZnZVdE3ArVYGKgeoj0YW1SYHS344Erx3G5aTyE/O6dvrA26FAhL1avLSYzkrJ4GcJL0E3F0sUvPayEx3D5Qcp6V/ShyH661sOdLIj85ID3eTWuXyeDnSYG81WdrxDtRY+HpvLQDjB6ZyRmbzQECrVtIvJVaWGYqoNWvWLG6//XbGjBnD2LFjefHFF7Fardxxxx0ATJ8+nT59+jB37lwgsAx8zJgxDBo0CKfTyX//+1/efvttXn31VSBQE/r+++/niSeeYMiQIcGSYTk5OVx77bXh+ppRR6dREatTYQthUiHRnFatJDVeS0qcFo1KCbEy8CG6xtNPP82SJUtYvXp1sGzixo0b+ctf/sKmTZs6dA8xd+5cHnvssVA1NeR8Pj/FFSYqG09/JY9OrSQ9QUeiXo3V6SVOp6Kk3s6yTbU02FzEadVckp9B/9S4Lmh59NKplaTEa0mJ00gOipMwm82YTKbgc51Oh07XsW0Inc1rE2oSdPdQZ/dL4nC9laKShogLuv1+P7VmFxUmO62tjFQrj9V2LTM6+LS4Cj9wVnYCo/snNztXoYT+qbGBmzMhotRNN91ETU0NjzzyCJWVlYwcOZIVK1YEf2GUlJSgVB77O261Wrn33nspLS1Fr9eTn5/PO++8w0033RQ853/+53+wWq3cfffdGI1Gzj//fFasWCE1ujsoNU6LzRmdSSmjhUIRWEKeFq/DEKOWAVTRqs5uxQF47rnnePrpp/n8888ZMWJE8PjXX39NdXU1/fr1Cx7zer08+OCDvPjiixw6dKjV60XzVhy318fWUuNJVxe2l06tZEB6HCu2V7BqVzU2l5dYrYqL8zP47eQzWfJ9CSNyk4jppTO5SmVgSX1qvJZ4nQwgtseJ/47mzJnDo48+Gp7GdDEJunuokf2S+aCojOIKMw63N2I6PJvLQ2mDvdWZoxajpVoVXj+kx+vQqhVMPDOjWWkwgJxEvZRVED3CzJkzmTlzZquvnZgg7YknnuCJJ5446fUUCgWPP/44jz/+eFc1sVdKjdcRo1FRZmy93xKdJ/sZRUccvxWnacVO01actvpOCCRUevLJJ/n0008ZM2ZMs9duu+22ZktQASZPnsxtt90WXGnUmhNn346fmYtkDreXTSUNXdaXpSfoWLG9go+2VgSP2VxePt5agQK4/uxcSht636BlrE5FapyWJL1GEqN1UHFxMX369Ak+7+gsN9DpvDahJtFKD9UnKYb0eB01Fic7yhoZnZdy6jeFkM/np9LkoMbixN/K7PbJRksfuuJMDtZa8Z7wvuQ4DekGyXwphAitOJ2aMzIN1FmcVDQ6Qpbht7eI1alIi9eSpNeilH3aogM6uhXnmWee4ZFHHmHx4sXk5eUFl5bGx8cTHx9PamoqqampzT5Do9GQlZXFmWee2b1fLsRMDjdFJUZcXVTlwO/3kxCjZtWu1su1rdpVzZQR2VQ2BpI+9nRqlYLkuMDWGL0MInaawWAgISHhtK5xfF6bpiC7Ka/Nr371qy5oZedI0N1DKRQKRvVL4rPiKjYfMYY16LY43BxpsJ+09uOpRkvPH5zebLQ0RqskNzm2lSsJIURopMbrSNJrqDI72xxAFK1TKiFJryXNoCVWK7ceonM6uhXn1VdfxeVyccMNNzS7Tk9astoetRYn20ob8fpOb8DQ6/NxuN7G3ioLTo+X3JRYbK7WZ81tLi9WlzewXbCHVllQKCAlTkufJD1p2QkyiNiNLBYL+/btCz4/ePAgRUVFpKSk0K9fv4jMayO/+XqwkX0DQfeWI0Y8Xh/qbl7i4vH6qGh0UGc5eQZ1tTKQybG9o6UqJQxIjZNMtkKIbqdSKclJ0pMap6XMaMd0ikSQvZ1OoyQtXkdyrKbbfweJnqkjW3Ha2pN9Mp15TyQrM9rZVWHqdIZyn99PaYONPVVm9tdYcR1ddmjQqUmIUROrVbUaeMdqVcRpVXhOXKbYA+i1KrITY8hJ0h/bvin3pN3qhx9+4KKLLgo+b8qxcPvtt7Nw4cKIzGsT1t+Aa9asYerUqeTk5KBQKFi+fPkp37N69WrOPvtsdDodgwcPZuHChSFvZ7QanB5PvE6Nze1lb7WlWz/baHexu8p8yoAbQKVUYnZ42jVaCtA3NVb2/wkhwkqnUTEwPZ6B6XHoNBJMnig+RsXA9DiGZieQbtBJwC1EGOyrtrCzvOMBtx8/ZUYbq/dU88Y3B/n3lnJ2VppxeX3E69SM7JvEFcOyMTs8TMrPbPUak/IzabR5eszScqUSshJjOLt/MhMGpzEwPT5i8iX1RhMnTsTv97d4NMWFTXltKisrcTgcfP7555xxxhlhbXNYZ7qtViuFhYXceeedXH/99ac8/+DBg0yZMoV77rmHRYsWsWrVKn7+85+TnZ3N5MmTu6HF0UWlUlKYm8Q3+2vZXGJkaPbp7ZFoD4fby+5KM/4a2ynPdXq8FFeYOFBj5f+mDG3XaGlGgo4kvTYUTRdCiA5L0GswxKipNbuoNNlb5J7obRL1ajISYiTBpRBh1JmSYH6/nyqTgz3VZvZVW7A4j63iiVGrGJIRz5DMeHKS9MGktjVmJ5OHBZJWfb6rKpiPZ1J+JpOHZXGwxtq1XywMYrRKUuN0nDUkXSrliNMS1t+KV1xxBVdccUW7z583bx4DBgzgz3/+MwBDhw5l7dq1vPDCCxJ0t2FUv6NB95EGbh7bN6SlWEobbOyrtuDx+kk7yXkNNhdbSo3sqjDjPrrPZ1elmUvyM5rt6W7SNFoac3Q5jxBCRBKFQkF6go6kOA2VjQ7qra5OL+WMRgolJOk1ZCbEyMyPEGHW0ZJgZQ12vjtUx4aDDYwoOhI8rlUpGZQexxmZBnKTY1G2cv/o9Pg4WGNlwuA0rhyRhdUVqDxjtHk4WGPFGaXT3ColwaRowRwUEnCL0xRVQ9Hr1q1rtbTD/fff3+Z7nE4nTqcz+NxsNoeqeRGpIDsBrVJJvdVFSb2N/qlxXf4ZNpeHnRWmk3bwfvyU1NnYUmrkcP2xWfCUOC2FuUmoFHD5sGwUKFodLT1SbyMvLVbqtwohIpZGpaRvSiyp8VrKjXYsjp5dYkyphJQ4HRkGLVq1BNtCnNJHH4X08i6PlwM1VlRuX6uTH7UXXQZAlcnBhkP1fH+onnLjsdlwtVLJwLQ4hmTE0z81FpXy1IGm0+OjtMFOZSOBpGleX9QuKY+PUZMapyVBr5G8QaLLRVXQXVlZGcxQ2SQzMxOTyYTdbkev17d4z9y5c3nssce6q4kRR6dRUdAngaIjRoqOGLs06Pb5/Byut3Gw1kJbiSldXi+7KsxsKTVitAeCcgWBRGgj+iaRmxxYpuTx0eZo6aE6K7nJejQquakTQkS+WK2awRkGGmwuLBoVDnfPCr5VSkgz6EiL18lySyEihM3l4WCtFben9WU2ZqebT3dU8v2heg7XHZv8UCsVDO+TyDl5KVyaU9Ppey2Pj6jMUq5RK0iO1ZIap5V8QSKkoiro7ozZs2cHM9oBlJWVUVBQEMYWdb+RfZMoOmJkc4mRa0b2OfUb2qHR7mZnhQmLo/XMvY12F1tLGymuMAUzXWpVSgqyExiRm0hiK/uy2xot7ZOkJ16n6ZJ2CyFEd0mO1TJ+UCqH66wcrrOddrmecNOoFWTEx5ASr5VZICEiiMnu5lCdtcUEiM3lYV+1hb3VFsob7awaHJi4UipgaHYCY/NSGNUvKbiEWnOodwSdCiUkxKhJjdNhiFHLKkrRLaIq6M7KyqKqqqrZsaqqKhISElqd5QbQ6XTodLrgc5PJFNI2RqLC3CQUwJEGG7UWJ2nxulO+py1en59Kk4Ndh+pb7Fn0+/3srDDz+a4qMtYfpunlJL2Gwtwk8rMNaNsxgnr8aGlynIb0hM63VwghwkmlVDAwPZB8aG+VhSpT+xMbRQqdRkmGQUdyrFbq0AoRYeosTkob7MF7Mofby/4aC3urzYHjR89TAGdkxjM2L4XR/ZMxxPS+yQydRklqnJbkOI2snhTdLqqC7vHjx/Pf//632bGVK1cyfvz4MLUoOiToNQzOiGdvtYWiEiOTClov73AqJrub0gY7Lo+vWcDtdHtZf7CeVTurKD+aKfMSoH9KLIW5SfRLjQ1muuyIGI2S3OTYTrVVCCEiSYxGxfDcRHKtenZXmdtcJRRJEvQa8tJiSdRrZCZIiDByur0olQrsLi96rQqfz49Oo6LcaKfa5MTl9XKo1sqeKguH6234jrtJy0yICWQez4jHMTk/jN8iPGK0ShJiNCTqNVJVQYRVWP/2WSwW9u3bF3x+8OBBioqKSElJoV+/fsyePZuysjLeeustAO655x7++te/8j//8z/ceeedfPHFF/zzn//kP//5T7i+QtQY1TeZvdUWNpc0dDjodnt9lBvtLRKl1VmcfLG7mq/31gZLfenUSiYMSuOnqf1Jju18aS+lEvLS4mQJoxCiR0mO0zJuQAplRjv7I7ScTkq8lrzUOFLitLBdSjQKEU5uj5cV2ytbJJm9fFgW20sb2VjSwKE6W7P91GlxWoZkGhiSYSBRf2xGO/rW2XScUgmGGDUJMYFyjpLkUUSKsAbdP/zwAxdddFHwedPe69tvv52FCxdSUVFBSUlJ8PUBAwbwn//8hwceeIC//OUv5Obm8tprr0m5sHYY2S+Jf248wp4qC1anp92jffVWJ+VGBx5vYNTUj5/yBjuvr97H5iPG4Ix3eryOi/MzmDA4lVitmuQvd3W6rQol9E2JldIzQogeSaFQkJscS2ZCDNXf6ag1O8NeYkyhgAxDDP3TYknohctOhYhETncg4P5wa3nwmM3l5cOt5fjwk5cax74aCxDYyndGpoEhGfGkxPWubXkxGiUJ+kCQHadVyzYYEZHCGnRPnDgR/0nuNBYuXNjqezZv3hzCVvVMmQkx5CTqKW+0s620kXMHpZ70fKfbS6nRjtkeWALp8frYU2VhS2kDtVYXmwbnAjA028CkoZkMz0nsdCenVSuJ1aqI06qJ1anQa1TSYQohejyNSkmfJD2pcVrKjutvu5NSCVkJevLSYo/VoxVCRASlMlBGtTVf7KrmuRsKOW9QKn2TY0k36Dq1lS8aKZUQr1NjiNGQqJfZbBEd5DdsLzKqXxLl2+xsKmloM+j2+/3Uml1UmOz4fIESE9tLG9lebsLhCSwhVyuV/OiMdC7JzyAnqfUEdm1RKEGvURGnUxOnVRGrVUlnKYTo1WI0Kgalx9Nod1PeYMfZDUVulUpIi9eRPyhNVhUJEaHsLm9w+96JbC4vdo+XCYPScLijr1RXR8VqVaQZdCTEqInXyWy2iD4SdPcio/om8Z9tFRyqs+L2eNGcEOzaXB5KG+xYnR4qGx0UHTGyv8YSzHxp0KkZkZtEQXYClnP7t+szNWoFcVq1zGILIcQpJOo1GHRqaixOqkyOFuV/uoJapQjU2I7TolYpQQJuISKW/ujkRGuBd9MKwT5Jeuxub+Dh8uI8IdlttFIqISlWS1qcjjSDNrASZ2fHJnqEiCQSdPci/VPjuP+SIZyRacDq8hKnUODz+dGolFSaHFQ02tlbZWFLqZFqszP4vj5JegpzkxiYFhfMYGtp5fqB5BWBDJHpabHEySy2EEJ0iFKpIDMhhpQ4DeVGR4sElp2lVStJN+hIiZMa20JEC5/Pz6T8zGZ7uptMys/E5/Nj0GswHJcszefzY3d7cRwXiNvd3pAM4nW1GI2K1HgtafHSV4meR4LuXsTr87G/xsI/vj7QLAPm5GFZfL23hq/31mJ3Ny0hV3BGpoHC3KQ263rrNEoS9RqS9NrADE3Mcct9TiNzuRBC9HYalYr+qXGkxXsoM9qxOVtfYnoqMRolGQmBGttS9kuI6KLTqLhyeBZAi+zlVw7ParFiEQIDd3E6dYuEuU63l8bcREwODxanB7PDjTPMy9IVSojTqgKZxvUa9EPSwtoeIUJJgu5eoikD5kdbK4LHjs+AeUamgc+Kq4jXqRneJ5GzchLQa4799Th+L3asVkX+ENkHKIQQoRanU3NGpoF6q5OKRgduT/vWjcbpVGQkxDQrFySEiD4atYrLh2Vx5YjsZnW6Wwu4T0anCfQJGQnHjrk8PswO99Eg3INaowz58nSNWoFBpyFBr8agU6NSKUP3YUJEEAm6e4n2ZMC8pjCHPsl6VAolGrWiWUbxWM0JSSsk4BZCiG6TEqcjMUZDldlJjcWJv40JKoNeTWaCjnidBNtC9BS6o/dcGv3RALWLbsG0aiWp8TpSm1Y0Zid0+fJ0hSKw/9yg15AQo5YqCaLXkr/5vcSpMmA6PD7GDkxFp1bKXmwhhIhAKpWSnKMlxsqNdhqPlhhTKCApVkO6QSc3tEKI03Ky5el2d+Besikgb2vljVqlwBCjDiwbj1EHkjYK0cvJb+de4lQZMGO1KlmGKIQQUUCnUTEgPR6T3Y3Z6SEtThucCRNCiFDQaVToNCqSYo8dc3t92I8LwrVqJQkxGmK1KskhIcQJJOjuJdqTAbOrlisJIYQIvQS9hgQZLBVChIlGpUSjV0o/JEQ7SNDdS3QmA6YQQgghhBBCiNMjmyx6kaYMmM/fOJIXbhzJ8zeO5PJhEnCL7vHKK6+Ql5dHTEwM48aN4/vvvw93k4QQIuJIXymEEO0TTf2lBN29jE6jQqMKLAXSqJSyD1B0i/fee49Zs2YxZ84cNm3aRGFhIZMnT6a6ujrcTRNCiIghfaUQQrRPtPWXEnQLIULu+eef5xe/+AV33HEHBQUFzJs3j9jYWN54441wN00IISKG9JVCCNE+0dZfStAthAgpl8vFxo0bmTRpUvCYUqlk0qRJrFu3LowtE0KIyCF9pRBCtE809pe9LpGaz+cDoKKi4pTnqmtrQ92ck/KUlp709XC2L5LbBtHdvkhuW5Omfz+NjY0kJCQEj+t0OnQ6XbNza2tr8Xq9ZGZmNjuemZnJrl27uqDFIlTa219G+t9Z6SvbJu3rPOkrRRPpK7tGJLcvktsG0d++jvSVEJ39Za8LuquqqgAYO3ZsmFsiRPQbNmxYs+dz5szh0UcfDU9jRJeT/lKIriF9Zc8mfaUQXaMn95W9LugeNWoU33//PZmZmSiVoVtdbzabKSgooLi4GIPBELLP6YnkZ9d53fWz8/l8lJSUUFBQgFp9rBtpbTQyLS0NlUoVvClpUlVVRVZWVsjaKE5fd/SX8u+98+Rnd3q64+cnfWXvIH1lZJOf3emJtL4SorO/7HVBt1qt5pxzzgn555hMJgD69OnTbJmEODX52XVed/7s+vXr167ztFoto0ePZtWqVVx77bVAoHNdtWoVM2fODGELxenqjv5S/r13nvzsTk93/fykr+z5/r+9uwtp6vHjOP6ZVkuHhSmaUWBRhBUpNRMxogeprJuiyEBieiPVFEO6CHqwoKir8qIaBeVVZRRUUmSUgVEUiqIZlNdBzAeCyEEPuPO7+IF/hv9+bPMcz856v2DgztS+O8gbvmxn0crExrmbmkRrpeTMXv51SzeA6dfY2Cifzyev16t169apublZoVBINTU1do8GAAmDVgJAdJzWS5ZuAJarrKzUyMiITp06pWAwqKKiIrW3t0/6AAwA+JvRSgCIjtN6ydJtEbfbraampj9ei4A/49zFL5HPXV1dXcK+5Qf2SeS/2UTHuZuaRD1/tBL/T6L+vToB525qEvn8OamXLsMwDLuHAAAAAAAgGVn38d0AAAAAAPzlWLoBAAAAALAISzcAAAAAABZh6bbAlStXlJ+fr9mzZ6ukpERdXV12j+QI58+fV3FxsTIyMpSTk6Ndu3ZpcHDQ7rEc6cKFC3K5XDpy5IjdowB/RCvjQyvNQyvhFPQydrTSPLRy6li6TXb37l01NjaqqalJvb29Kiws1LZt2zQ8PGz3aAmvs7NTfr9f79690/Pnz/X7929t3bpVoVDI7tEcpbu7W9euXdPq1avtHgX4I1oZP1ppDloJp6CX8aGV5qCV5uDTy01WUlKi4uJiXb58WZIUDoe1aNEi1dfX69ixYzZP5ywjIyPKyclRZ2enNmzYYPc4jjA2NqY1a9bo6tWrOnv2rIqKitTc3Gz3WMAktNI8tDJ2tBJOQi/NQStjRyvNwyvdJvr165d6enpUXl4+cSwlJUXl5eV6+/atjZM507dv3yRJ8+bNs3kS5/D7/dq5c2fE3yCQaGiluWhl7GglnIJemodWxo5WmmeG3QMkk9HRUY2Pjys3NzfieG5urj59+mTTVM4UDod15MgRlZWVadWqVXaP4witra3q7e1Vd3e33aMA/4lWmodWxo5WwknopTloZexopblYupGQ/H6/Pnz4oNevX9s9iiN8/vxZDQ0Nev78uWbPnm33OACmCa2MDa0E/k60Mja00nws3SbKzs5WamqqhoaGIo4PDQ1p/vz5Nk3lPHV1dXr8+LFevXqlhQsX2j2OI/T09Gh4eFhr1qyZODY+Pq5Xr17p8uXL+vnzp1JTU22cEPgfWmkOWhk7WgmnoZdTRytjRyvNxzXdJpo1a5bWrl2rjo6OiWPhcFgdHR0qLS21cTJnMAxDdXV1evDggV6+fKnFixfbPZJjbNmyRQMDA+rr65u4eb1eVVVVqa+vjzAiodDKqaGV8aOVcBp6GT9aGT9aaT5e6TZZY2OjfD6fvF6v1q1bp+bmZoVCIdXU1Ng9WsLz+/26ffu2Hj16pIyMDAWDQUnS3LlzlZaWZvN0iS0jI2PSNUoej0dZWVlcu4SERCvjRyvjRyvhRPQyPrQyfrTSfCzdJqusrNTIyIhOnTqlYDCooqIitbe3T/oADEwWCAQkSRs3bow43tLSourq6ukfCIBlaGX8aCXwd6GX8aGVSCT8P90AAAAAAFiEa7oBAAAAALAISzcAAAAAABZh6QYAAAAAwCIs3QAAAAAAWISlGwAAAAAAi7B0AwAAAABgEZZuAAAAAAAswtINAAAAAIBFWLqRNFwulx4+fGj3GACQ0GglAESHXsIsLN0wRXV1tVwu16Tb9u3b7R4NABIGrQSA6NBLJJMZdg+A5LF9+3a1tLREHHO73TZNAwCJiVYCQHToJZIFr3TDNG63W/Pnz4+4ZWZmSvr37TmBQEAVFRVKS0vTkiVLdP/+/YifHxgY0ObNm5WWlqasrCzV1tZqbGws4ntu3ryplStXyu12Ky8vT3V1dRGPj46Oavfu3UpPT9eyZcvU1tZm7ZMGgBjRSgCIDr1EsmDpxrQ5efKk9uzZo/7+flVVVWn//v36+PGjJCkUCmnbtm3KzMxUd3e37t27pxcvXkSELxAIyO/3q7a2VgMDA2pra9PSpUsj/o0zZ85o3759ev/+vXbs2KGqqip9/fp1Wp8nAEwFrQSA6NBLOIYBmMDn8xmpqamGx+OJuJ07d84wDMOQZBw8eDDiZ0pKSoxDhw4ZhmEY169fNzIzM42xsbGJx588eWKkpKQYwWDQMAzDWLBggXH8+PE/ziDJOHHixMT9sbExQ5Lx9OlT054nAEwFrQSA6NBLJBOu6YZpNm3apEAgEHFs3rx5E1+XlpZGPFZaWqq+vj5J0sePH1VYWCiPxzPxeFlZmcLhsAYHB+VyufTlyxdt2bLlP2dYvXr1xNcej0dz5szR8PBwvE8JAExHKwEgOvQSyYKlG6bxeDyT3pJjlrS0tKi+b+bMmRH3XS6XwuGwFSMBQFxoJQBEh14iWXBNN6bNu3fvJt0vKCiQJBUUFKi/v1+hUGji8Tdv3iglJUXLly9XRkaG8vPz1dHRMa0zA8B0o5UAEB16CafglW6Y5ufPnwoGgxHHZsyYoezsbEnSvXv35PV6tX79et26dUtdXV26ceOGJKmqqkpNTU3y+Xw6ffq0RkZGVF9frwMHDig3N1eSdPr0aR08eFA5OTmqqKjQ9+/f9ebNG9XX10/vEwWAKaCVABAdeolkwdIN07S3tysvLy/i2PLly/Xp0ydJ/376Y2trqw4fPqy8vDzduXNHK1askCSlp6fr2bNnamhoUHFxsdLT07Vnzx5dvHhx4nf5fD79+PFDly5d0tGjR5Wdna29e/dO3xMEABPQSgCIDr1EsnAZhmHYPQSSn8vl0oMHD7Rr1y67RwGAhEUrASA69BJOwjXdAAAAAABYhKUbAAAAAACL8PZyAAAAAAAswivdAAAAAABYhKUbAAAAAACLsHQDAAAAAGARlm4AAAAAACzC0g0AAAAAgEVYugEAAAAAsAhLNwAAAAAAFmHpBgAAAADAIizdAAAAAABY5B/051W4LA78BgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x250 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# Create a plot for each task\n",
    "fig, axes = plt.subplots(1, len(tasks), figsize=(10, 2.5))\n",
    "labels = ['Random', 'Less']\n",
    "losses = [random_selection_losses, less_selection_losses]\n",
    "i = 1\n",
    "task_names = [\"MMLU\", \"TyDiQA\", \"BBH\"]\n",
    "for data in losses[1:]:\n",
    "    df = dfs[i] \n",
    "\n",
    "    # Loop through each task and plot\n",
    "    for j, (ax, task) in enumerate(zip(axes, data.keys())):\n",
    "        task_data = df[df['task'] == task]\n",
    "        sns.lineplot(x='epoch', y='loss', data=task_data, ax=ax, marker='o', )\n",
    "        lower_bound = task_data[\"loss\"] - task_data[\"std\"]\n",
    "        upper_bound = task_data[\"loss\"] + task_data[\"std\"]\n",
    "        x_axis = task_data[\"epoch\"]\n",
    "        ax.fill_between(x_axis.values, lower_bound.values, upper_bound.values, alpha=.3)\n",
    "        # ax.invert_yaxis()\n",
    "        axx = ax.twinx()\n",
    "        axx.bar(x_axis, task_data[\"accuracy\"], alpha=0.3, color=\"red\", yerr=task_data[\"accuracy_std\"], ecolor=\"gray\", )\n",
    "\n",
    "        # Set title and labels\n",
    "        ax.set_title(task_names[j])\n",
    "        ax.set_xlabel('Epoch')\n",
    "        ax.set_ylabel('Loss')\n",
    "        axx.set_ylabel('Accuracy')\n",
    "        # ax.legend(loc='lower right')\n",
    "        # axx.legend(loc='lower right')\n",
    "\n",
    "\n",
    "\n",
    "# Adjust layout to prevent overlap\n",
    "plt.tight_layout()\n",
    "plt.savefig(f\"{model_name}_loss_acc.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'llama-2-13b-hf'"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Add a new field to indicate the type of dataframe\n",
    "dfs[0]['method'] = 'Random'\n",
    "dfs[1]['method'] = 'Less'\n",
    "\n",
    "# Concatenate the dataframes in rows\n",
    "merged_df = pd.concat([dfs[0], dfs[1]], ignore_index=True)\n",
    "\n",
    "merged_df.to_csv(f\"/scratch/gpfs/mengzhou/space10/final/loss/{model_name}/loss_acc.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>task</th>\n",
       "      <th>checkpoint</th>\n",
       "      <th>loss</th>\n",
       "      <th>std</th>\n",
       "      <th>epoch</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>accuracy_std</th>\n",
       "      <th>method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mmlu</td>\n",
       "      <td>0</td>\n",
       "      <td>2.048611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>55.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mmlu</td>\n",
       "      <td>105</td>\n",
       "      <td>1.210491</td>\n",
       "      <td>0.007815</td>\n",
       "      <td>1</td>\n",
       "      <td>57.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mmlu</td>\n",
       "      <td>211</td>\n",
       "      <td>1.246322</td>\n",
       "      <td>0.015807</td>\n",
       "      <td>2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mmlu</td>\n",
       "      <td>317</td>\n",
       "      <td>1.276657</td>\n",
       "      <td>0.015604</td>\n",
       "      <td>3</td>\n",
       "      <td>54.7</td>\n",
       "      <td>0.9</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mmlu</td>\n",
       "      <td>420</td>\n",
       "      <td>1.299274</td>\n",
       "      <td>0.013643</td>\n",
       "      <td>4</td>\n",
       "      <td>54.5</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tydiqa</td>\n",
       "      <td>0</td>\n",
       "      <td>0.418627</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tydiqa</td>\n",
       "      <td>105</td>\n",
       "      <td>0.384097</td>\n",
       "      <td>0.002522</td>\n",
       "      <td>1</td>\n",
       "      <td>25.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>tydiqa</td>\n",
       "      <td>211</td>\n",
       "      <td>0.387749</td>\n",
       "      <td>0.004267</td>\n",
       "      <td>2</td>\n",
       "      <td>26.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>tydiqa</td>\n",
       "      <td>317</td>\n",
       "      <td>0.390958</td>\n",
       "      <td>0.001645</td>\n",
       "      <td>3</td>\n",
       "      <td>27.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>tydiqa</td>\n",
       "      <td>420</td>\n",
       "      <td>0.388959</td>\n",
       "      <td>0.002763</td>\n",
       "      <td>4</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>bbh</td>\n",
       "      <td>0</td>\n",
       "      <td>0.245116</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>bbh</td>\n",
       "      <td>105</td>\n",
       "      <td>0.249943</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>1</td>\n",
       "      <td>37.9</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>bbh</td>\n",
       "      <td>211</td>\n",
       "      <td>0.251569</td>\n",
       "      <td>0.001109</td>\n",
       "      <td>2</td>\n",
       "      <td>39.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>bbh</td>\n",
       "      <td>317</td>\n",
       "      <td>0.255294</td>\n",
       "      <td>0.000987</td>\n",
       "      <td>3</td>\n",
       "      <td>42.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>bbh</td>\n",
       "      <td>420</td>\n",
       "      <td>0.257143</td>\n",
       "      <td>0.000986</td>\n",
       "      <td>4</td>\n",
       "      <td>40.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>mmlu</td>\n",
       "      <td>0</td>\n",
       "      <td>2.048611</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>55.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mmlu</td>\n",
       "      <td>105</td>\n",
       "      <td>1.063763</td>\n",
       "      <td>0.022103</td>\n",
       "      <td>1</td>\n",
       "      <td>55.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>mmlu</td>\n",
       "      <td>211</td>\n",
       "      <td>1.058478</td>\n",
       "      <td>0.009877</td>\n",
       "      <td>2</td>\n",
       "      <td>54.9</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>mmlu</td>\n",
       "      <td>317</td>\n",
       "      <td>1.153999</td>\n",
       "      <td>0.027506</td>\n",
       "      <td>3</td>\n",
       "      <td>54.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>mmlu</td>\n",
       "      <td>420</td>\n",
       "      <td>1.264791</td>\n",
       "      <td>0.061943</td>\n",
       "      <td>4</td>\n",
       "      <td>55.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>tydiqa</td>\n",
       "      <td>0</td>\n",
       "      <td>0.418627</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>tydiqa</td>\n",
       "      <td>105</td>\n",
       "      <td>0.348199</td>\n",
       "      <td>0.006847</td>\n",
       "      <td>1</td>\n",
       "      <td>31.8</td>\n",
       "      <td>7.3</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>tydiqa</td>\n",
       "      <td>211</td>\n",
       "      <td>0.321107</td>\n",
       "      <td>0.017958</td>\n",
       "      <td>2</td>\n",
       "      <td>33.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>tydiqa</td>\n",
       "      <td>317</td>\n",
       "      <td>0.325865</td>\n",
       "      <td>0.025372</td>\n",
       "      <td>3</td>\n",
       "      <td>32.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>tydiqa</td>\n",
       "      <td>420</td>\n",
       "      <td>0.337234</td>\n",
       "      <td>0.027540</td>\n",
       "      <td>4</td>\n",
       "      <td>31.5</td>\n",
       "      <td>5.3</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>bbh</td>\n",
       "      <td>0</td>\n",
       "      <td>0.245116</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>22.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>bbh</td>\n",
       "      <td>105</td>\n",
       "      <td>0.243699</td>\n",
       "      <td>0.000988</td>\n",
       "      <td>1</td>\n",
       "      <td>45.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>bbh</td>\n",
       "      <td>211</td>\n",
       "      <td>0.244054</td>\n",
       "      <td>0.001145</td>\n",
       "      <td>2</td>\n",
       "      <td>44.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>bbh</td>\n",
       "      <td>317</td>\n",
       "      <td>0.246646</td>\n",
       "      <td>0.001242</td>\n",
       "      <td>3</td>\n",
       "      <td>41.6</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>bbh</td>\n",
       "      <td>420</td>\n",
       "      <td>0.250102</td>\n",
       "      <td>0.001590</td>\n",
       "      <td>4</td>\n",
       "      <td>44.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Less</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      task  checkpoint      loss       std  epoch  accuracy  accuracy_std   \n",
       "0     mmlu           0  2.048611  0.000000      0      55.8           0.0  \\\n",
       "1     mmlu         105  1.210491  0.007815      1      57.3           1.3   \n",
       "2     mmlu         211  1.246322  0.015807      2      55.0           0.7   \n",
       "3     mmlu         317  1.276657  0.015604      3      54.7           0.9   \n",
       "4     mmlu         420  1.299274  0.013643      4      54.5           0.7   \n",
       "5   tydiqa           0  0.418627  0.000000      0      53.1           0.0   \n",
       "6   tydiqa         105  0.384097  0.002522      1      25.8           8.4   \n",
       "7   tydiqa         211  0.387749  0.004267      2      26.9           3.5   \n",
       "8   tydiqa         317  0.390958  0.001645      3      27.1           3.4   \n",
       "9   tydiqa         420  0.388959  0.002763      4      36.0           3.0   \n",
       "10     bbh           0  0.245116  0.000000      0      22.2           0.0   \n",
       "11     bbh         105  0.249943  0.000857      1      37.9           4.0   \n",
       "12     bbh         211  0.251569  0.001109      2      39.5           5.4   \n",
       "13     bbh         317  0.255294  0.000987      3      42.0           5.2   \n",
       "14     bbh         420  0.257143  0.000986      4      40.3           2.6   \n",
       "15    mmlu           0  2.048611  0.000000      0      55.8           0.0   \n",
       "16    mmlu         105  1.063763  0.022103      1      55.8           1.1   \n",
       "17    mmlu         211  1.058478  0.009877      2      54.9           0.7   \n",
       "18    mmlu         317  1.153999  0.027506      3      54.6           0.9   \n",
       "19    mmlu         420  1.264791  0.061943      4      55.2           2.9   \n",
       "20  tydiqa           0  0.418627  0.000000      0      53.1           0.0   \n",
       "21  tydiqa         105  0.348199  0.006847      1      31.8           7.3   \n",
       "22  tydiqa         211  0.321107  0.017958      2      33.6           4.8   \n",
       "23  tydiqa         317  0.325865  0.025372      3      32.1           5.4   \n",
       "24  tydiqa         420  0.337234  0.027540      4      31.5           5.3   \n",
       "25     bbh           0  0.245116  0.000000      0      22.2           0.0   \n",
       "26     bbh         105  0.243699  0.000988      1      45.1           4.4   \n",
       "27     bbh         211  0.244054  0.001145      2      44.4           1.2   \n",
       "28     bbh         317  0.246646  0.001242      3      41.6           0.7   \n",
       "29     bbh         420  0.250102  0.001590      4      44.4           1.2   \n",
       "\n",
       "    method  \n",
       "0   Random  \n",
       "1   Random  \n",
       "2   Random  \n",
       "3   Random  \n",
       "4   Random  \n",
       "5   Random  \n",
       "6   Random  \n",
       "7   Random  \n",
       "8   Random  \n",
       "9   Random  \n",
       "10  Random  \n",
       "11  Random  \n",
       "12  Random  \n",
       "13  Random  \n",
       "14  Random  \n",
       "15    Less  \n",
       "16    Less  \n",
       "17    Less  \n",
       "18    Less  \n",
       "19    Less  \n",
       "20    Less  \n",
       "21    Less  \n",
       "22    Less  \n",
       "23    Less  \n",
       "24    Less  \n",
       "25    Less  \n",
       "26    Less  \n",
       "27    Less  \n",
       "28    Less  \n",
       "29    Less  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "data_selection",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
